Maintain Population Diversity Research Articles

Energy consumption forecast model of CNC machine tools based on support vector regression optimized by improved artificial hummingbird algorithm

With the development of the manufacturing industry, energy consumption is growing rapidly, which makes the energy crisis and environmental problems become more and more serious. CNC machine tools play an essential role and are the primary energy consumption devices in the manufacturing industry. The accurate prediction of machine tool energy consumption can provide support for energy production plans and reduce energy waste. This paper proposes a novel energy consumption prediction model based on support vector regression (SVR) optimized by an improved artificial hummingbird algorithm (IAHA). Firstly, as the artificial hummingbird algorithm (AHA) may easily get trapped in a local optimum, an improved AHA based on chaotic mapping and local backtracking exploitation strategy is proposed. The chaotic mapping is used to initialize individual positions, which is good for maintaining population diversity. The local backtracking exploitation strategy is employed to improve the local optimization ability. The effectiveness and feasibility of the IAHA algorithm have been verified through 12 benchmark functions. Then, the IAHA algorithm is employed to optimize the parameters of the SVR, and the IAHA-SVR energy consumption prediction model is established. Finally, the effectiveness and feasibility of the IAHA-SVR model are verified through a case study.

An enhanced sparrow search swarm optimizer via multi-strategies for high-dimensional optimization problems

With the development of science and technology, high-dimensional global optimization problems have become increasingly prevalent for scientific research and engineering, such as gene recognition, vehicle routing, job scheduling, and network topology. These problems are typically characterized by enormous and complex search spaces and numerous local minima, making it challenging to find the global optimal solution with limited computing resources. This paper introduces an enhanced sparrow search swarm optimizer (ESSSO) based on a bio-mimetic method. The ESSSO employs an adaptive sinusoidal walk strategy based on the von Mises distribution, a learning strategy utilizing roulette wheel selection, a two-stage evolution strategy, and a selection mutation strategy to address these issues. The proposed sinusoidal walk strategy, grounded in the von Mises distribution, supports a balanced evolutionary search. This mechanism disperses the individuals in a swarm in various directions based on a circular normal distribution. It then leads the search and adaptively adjusts their step sizes according to the size of the search domain during each generation of evolution. The learning strategy, based on roulette wheel selection, enhances the diversity of the population and improves the global search capability of the algorithm during the initial iterations. The two-stage evolution strategy involves a sine-learning mechanism based on the von Mises distribution and an adaptive mutation mechanism. The former is designed to boost the convergence speed of ESSSO, while the latter prevents ESSSO from getting trapped in a local optimum. Additionally, the selection mutation strategy further enhances convergence speed while maintaining population diversity. These strategies promote exploration in the early stages of evolution and exploitation in the later stages, enabling a well-balanced search for optimal solutions. We conducted comprehensive experiments two standard benchmark sets (i.e., CEC2010 and CEC2013), antenna array optimization, feature selection, and four engineering design problems. The results indicate that ESSSO outperforms ten comparison algorithms, especially in scenarios with smaller population sizes. This confirms its effectiveness in high-dimensional global optimization tasks and demonstrates that it can achieve better results with less computational resource consumption.

Multi-strategy enhanced Marine Predators Algorithm with applications in engineering optimization and feature selection problems

This paper introduces a novel meta-heuristic optimization algorithm named Clustering Wavelet Opposition-based Marine Predators Algorithm (CWOMPA) to address some limitations present in the well-established Marine Predators Algorithm (MPA). CWOMPA incorporates three key strategies: a fuzzy clustering approach for escaping local optima, using wavelet basis function-based impact coefficient adjustment for elites to prevent premature convergence, and finally an adaptive opposition-based learning strategy for maintaining population diversity. Compared with some recent meta-heuristic algorithms, extensive evaluations conducted affirm that CWOMPA achieves the best Friedman rank, 4.30 and 1.95 respectively, on 23 benchmark functions and the CEC 2017 benchmark set. Not only does CWOMPA demonstrate significant effectiveness on six constrained problems from the CEC 2020 real-world benchmarks, but also when applied to 14 medical datasets, it gains superior Friedman ranks in terms of selected features, classification accuracy, F-Score, and objective function value, 2.5, 2.25, 2.96, 2.93 respectively, outperforming other common methods. Finally, CWOMPA exhibits the highest classification accuracy across all datasets and the best F-Score performance in nine datasets compared to traditional feature selection algorithms. The obtained results reveal that CWOMPA is a powerful and versatile optimization algorithm with significant potential in various real-world applications, including feature selection for medical datasets.

The Green Flexible Job-Shop Scheduling Problem Considering Cost, Carbon Emissions, and Customer Satisfaction under Time-of-Use Electricity Pricing

Exploration of the green flexible job-shop scheduling problem is essential for enterprises aiming for sustainable practices, including energy conservation, emissions reduction, and enhanced economic and social benefits. While existing research has predominantly focused on carbon emissions or energy consumption as green scheduling objectives, this paper addresses the broader scope by incorporating the impact of variable energy prices on energy cost. Through the introduction of an energy cost model based on time-of-use electricity pricing, the study formulates a multi-objective optimization model for green flexible job-shop scheduling. The objectives include minimizing cost, reducing carbon emissions, and maximizing customer satisfaction. To prevent premature convergence and maintain population diversity, an enhanced genetic algorithm is employed for solving. The validation of the algorithm’s effectiveness is demonstrated through specific examples, providing decision results for optimal scheduling under various weight combinations. The research outcomes hold substantial practical value as they can significantly reduce energy expenses, lower carbon emissions, and elevate customer satisfaction while safeguarding production efficiency. This contributes to enhancing the market competitiveness and green brand image of businesses.

An adaptative differential evolution with enhanced diversity and restart mechanism

Differential Evolution (DE) stands out as an exceptional intelligent evolutionary algorithm, acclaimed for its simplicity in implementation and the ability to optimize without necessitating differentiable conditions. However, a significant pitfall of DE lies in its susceptibility to getting trapped in local minima, leading to algorithmic stagnation and substantial performance impairments. To counteract the shortcoming, the Adaptative Differential Evolution with Diversity Maintenance and Restart Mechanism (ADE-DMRM) is proposed. This approach integrates three primary innovations. Firstly, the algorithm controls the number of generations that individuals stay in the external archive based on the successful evolution rate, thereby optimizing the mutation strategy to effectively maintain population diversity. Secondly, ADE-DMRM proposes a novel restart mechanism in which the current stagnant individuals are identified by combining a stagnation tracker and a diversity assessment indicator and then regenerated using a dimension-learning-based approach. Thirdly, wavelet basis functions and Cauchy distributions are employed for scaling factor implementation across different stages, while the dimension change information of successfully evolved individuals is harnessed to refine the adaptive parameter control scheme. Finally, comprehensive experiments were undertaken on the CEC2013, CEC2014, and CEC2017 test sets, assessing accuracy, convergence speed, the efficacy of each module, and time complexity. The results confirm that ADE-DMRM is an efficient single-objective optimization algorithm that outperforms current advanced variants.

WHRIME: A weight-based recursive hierarchical RIME optimizer for breast cancer histopathology image segmentation

In medical image processing, multi-threshold image segmentation has been challenging, as selecting appropriate thresholds is crucial for distinguishing different structures within an image, especially when dealing with breast cancer images. Breast cancer images are complex with multiple tissue types, which pose challenges to precise diagnosis. A weight-based recursive hierarchical bootstrapping rime algorithm (WHRIME) is proposed to tackle this case effectively. The proposed WHRIME segregates the population into elite and non-elite individuals. The hierarchical bootstrapping strategy ensures comprehensive exploration of the solution space, with elite individuals guiding the position updates of non-elite individuals. A weighted method is introduced based on solution quality differences to maintain population diversity and enhance convergence accuracy. We apply WHRIME in multi-threshold image segmentation on breast cancer histopathology images. Experimental results on both the IEEE CEC 2017 benchmark suit and breast cancer histopathology images from the Databiox dataset validate the superiority of WHRIME over competing algorithms, affirming WHRIME’s capability in addressing complex breast cancer histopathology image segmentation problems.

Collaborative resource allocation-based differential evolution for solving numerical optimization problems

Differential evolution (DE) is an efficient and powerful population-based search algorithm for solving numerical optimization problems in continuous spaces. It has been proven that multi-strategy DE algorithms are more effective than single-strategy DE algorithms in addressing benchmark and real-world problems. However, most multi-strategy DE variants focus on maintaining population diversity and balancing exploitation and exploration, ignoring the dynamic allocation of computational resources. Moreover, the success of these algorithms often depends on additional designed techniques, leading to increased computational complexity. In this paper, the Collaborative Resource Allocation-based Differential Evolution (CRADE) is introduced. It involves a collaborative resource allocation mechanism that utilizes the historical performance ranking of three mutation strategies to automatically allocate computational resources to various subpopulations during the search process. The parameter adaptation technique is used to adjust the associated control parameters of different mutation strategies. As a result, the most efficient mutation strategy consumes the majority of computational resources at different search stages to mitigate inefficient search under constrained resources. The performance of CRADE is evaluated on the well-known CEC2013 benchmark function set. The paper also investigates its application in the parameter identification of photovoltaic solar cells and modules. The overall results show that CRADE exhibits superior and competitive performance compared to other state-of-the-art algorithms. Consequently, CRADE has emerged as a novel and effective approach for addressing numerical optimization problems, distinguished by its excellence, practicality, and unwavering reliability.

EABOA: Enhanced adaptive butterfly optimization algorithm for numerical optimization and engineering design problems

The butterfly optimization algorithm (BOA) is a meta-heuristic algorithm that mimics foraging and mating behavior of butterflies. In order to alleviate the problems of slow convergence, local optimum and lack of population diversity of BOA, an enhanced adaptive butterfly optimization algorithm (EABOA) is proposed in this paper. First, a new adaptive fragrance model is designed, which provided a finer fragrance perception way and effectively enhanced the convergence speed and accuracy. Second, Lévy flight with high-frequency short-step jumping and low-frequency long-step walking is adopted to help the algorithm jump out of the local optimum. Third, the dimension learning-based hunting is employed to enhance information exchange by creating neighbors for each butterfly, thus improving the balance between local and global search and maintaining population diversity. In addition, the Fitness-Distance-Constraint (FDC) method is introduced to enhance constraint handling in EABOA (named FDC-EABOA). The proposed EABOA is compared with 8 well-known algorithms and 8 BOA variants in CEC 2022 test suite and the results were statistically analyzed using Friedman, Friedman aligned rank, Wilcoxon signed rank, Quade rank and multiple comparisons, analysis of variance (ANOVA) and range analysis. Finally, EABOA and FDC-EABOA are applied to seven engineering problems (parameter identification of photovoltaic module model, speed reducer design, tension/compression spring design, pressure vessel design, gear train design, welded beam design, SOPWM for 3-level inverters), and metrics such as Improvement Index (IF) and Mean Constraint Violation (MV) confirm that the proposed algorithms are satisfactory. Experimental results and statistical analysis show that the proposed algorithms outperform the comparison algorithms and demonstrate the strong potential for solving numerical optimization and engineering design problems.

An evolutionary crow search algorithm equipped with interactive memory mechanism to optimize artificial neural network for disease diagnosis

Artificial neural network (ANN) is an information processing paradigm that loosely models the thinking patterns of the human brain with specifications such as real-time learning, self-adaption, and self-organization. The learning process of ANNs is complex and tackles shortcomings such as a slow convergence rate, learning time-consuming, and local minimum trapping, especially when using gradient-based optimization techniques. Although many metaheuristics have been proposed to arm ANN and solve these weaknesses, ANN learning still needs solution quality. Therefore, this study proposes an evolutionary crow search algorithm (ECSA) to optimize the hyperparameters of ANNs for diagnosing chronic diseases. ECSA introduces an evolutionary search strategy, self-adaptive adaptive flight length, and an interactive memory mechanism to alleviate the canonical crow search algorithm's shortcomings. The evolutionary search strategy and self-adaptive flight length provide a meaningful search strategy in which crows effectively explore and exploit problem spaces by maintaining population diversity. During the search process, the interactive memory mechanism records the best solution obtained during optimization. The performance of ECSA was evaluated and compared with well-known metaheuristic algorithms in terms of local and global search abilities, local optima avoidance, and convergence speed towards the promising area. Then, the results were statistically analyzed. Ultimately, the effectiveness of an adaptation of ECSA named ECSA-MLP for optimizing hyperparameters of the multilayer perceptron network for diagnosing diseases including coronavirus, breast cancer, diabetes, cardiovascular, cervical cancer, Parkinson's, mammography, and acute inflammation was compared with state-of-the-art competitor algorithms. The experimental results indicated the superiority of ECSA over competitor algorithms in optimizing the network.

A Feature Selection Algorithm Based on Differential Evolution for English Speech Emotion Recognition

The automatic identification of emotions from speech holds significance in facilitating interactions between humans and machines. To improve the recognition accuracy of speech emotion, we extract mel-frequency cepstral coefficients (MFCCs) and pitch features from raw signals, and an improved differential evolution (DE) algorithm is utilized for feature selection based on K-nearest neighbor (KNN) and random forest (RF) classifiers. The proposed multivariate DE (MDE) adopts three mutation strategies to solve the slow convergence of the classical DE and maintain population diversity, and employs a jumping method to avoid falling into local traps. The simulations are conducted on four public English speech emotion datasets: eNTERFACE05, Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS), Surrey Audio-Visual Expressed Emotion (SAEE), and Toronto Emotional Speech Set (TESS), and they cover a diverse range of emotions. The MDE algorithm is compared with PSO-assisted biogeography-based optimization (BBO_PSO), DE, and the sine cosine algorithm (SCA) on emotion recognition error, number of selected features, and running time. From the results obtained, MDE obtains the errors of 0.5270, 0.5044, 0.4490, and 0.0420 in eNTERFACE05, RAVDESS, SAVEE, and TESS based on the KNN classifier, and the errors of 0.4721, 0.4264, 0.3283 and 0.0114 based on the RF classifier. The proposed algorithm demonstrates excellent performance in emotion recognition accuracy, and it finds meaningful acoustic features from MFCCs and pitch.

Optimal deployment strategy of airships for near‐space communication system using multiobjective advanced evolutionary algorithm

SummaryThis paper presents a near‐space communication system (NSCS) using advanced deployment strategies to gain high throughput. The airships are deployed according to the user's location, assuming robust backbone network characteristics such as signal path loss, fading factor, routing efficiency, and safety issues among bi‐connected airships. Due to the independent flying nature of airships, it is very attractive to deploy them as aerial base stations and construct airborne networks to provide service for on‐ground users. However, it is quite challenging to optimally deploy multiple airships for on‐demand coverage while maintaining the connectivity among airships. A balance between the network parameters, i.e., capacity and coverage area, should be maintained for optimal deployment of the airships. We have derived the maximum throughput of NSCS, including system parameters, as a multiobjective optimization problem subjected to efficient routing protocol and safety constraints. A decomposition‐based advanced multiobjective evolutionary algorithm (AMOEA/D) is adopted to solve the deployment optimization problem. The proposed algorithm is motivated by the non‐dominated solutions that maintain population diversity over the variable space. Two designed test problems, that is, the L‐shaped hotspot problem and nine hotspot problems, are also investigated. Numerical results show that the proposed method improves the system performance compared with benchmark external archive‐guided MOEA/D (EGA‐MOEA/D) and non‐dominated sorted genetic algorithms (NSGA‐II) by 10.46% and 3.84%, respectively.

Parallel dynamic NSGA-II with multi-population search for rescheduling of Seru production considering schedule changes under different dynamic events

Seru production is usually accompanied by various dynamic events in the real world, such as worker tool breakdowns, new batch arrivals, batch cancellations, and batch size changes. Therefore, dynamic Seru production is proposed. To cope with the dynamic seru production, we adopt an event-driven predictive-reactive scheduling method, i.e., when a dynamic event occurs, rescheduling is triggered to produce a new schedule with good efficiency. However, the new schedule may deviate significantly from the previous schedule. Therefore, it is critical to minimize schedule changes while maximizing efficiency (e.g. makespan) simultaneously. First, we formulate a multi-objective dynamic seru production model that applies to various dynamic events to minimize both schedule changes and makespan. Then, we develop dynamic NSGA-II to produce a new schedule. To effectively respond to dynamic events, a population re-initialization strategy is proposed to fast-track non-dominated solutions by making use of the characteristics of dynamic events, and a communication mechanism is designed to accelerate the convergence speed. However, these two mechanisms may lead to reducing its diversity. To maintain population diversity, a multi-population search strategy based on hyper-heuristic is adopted. The hyper-heuristic algorithm is incorporated to evolve an additional seru rescheduling population, which employs NSGA-ΙΙ as the high-level strategy to manipulate several low-level heuristics to generate efficient local search heuristic sequences. However, this approach also results in a longer computation time. Therefore, a parallel mechanism is designed to save computational time. Finally, we have conducted extensive experiments to demonstrate that our algorithm outperforms existing algorithms and provides several management insights.

Multiobjective-Based Constraint-Handling Technique for Evolutionary Constrained Multiobjective Optimization: A New Perspective

Multiobjective-based constraint-handling techniques are popular in evolutionary constrained single-objective optimization. However, most of these techniques run into troubles when dealing with constrained multiobjective optimization problems (CMOPs). That is, they have difficulty optimizing too many objective functions, are ineffective in maintaining population diversity, or are challenged in establishing appropriate additional objective functions. As a remedy to these limitations, we propose a novel technique called NRC for handling CMOPs. The novelty of NRC lies in its three sorting procedures: nondominated sorting, reversed nondominated sorting, and constrained crowding distance sorting, which are performed in sequence to provide driving forces toward the Pareto front (PF) of a transformed unconstrained multiobjective optimization problem (treating the overall constraint violation as an additional objective function), the boundary front, and the constrained PF, respectively. With the combination of these three different forces, NRC can conveniently approach the desired PF from diverse search directions. The effectiveness of NRC is experimentally verified. Also, we incorporate NRC into a two-archive mechanism and develop a novel constrained multiobjective evolutionary algorithm, called NRC2. Comprehensive experiments on 49 benchmark CMOPs and 21 real-world ones demonstrate that NRC2 is significantly superior or comparable to six state-of-the-art constrained evolutionary multiobjective optimizers on most test instances.

Improving Performance of Differential Evolution Using Multi-Population Ensemble Concept

Differential evolution (DE) stands out as a straightforward yet remarkably powerful evolutionary algorithm employed for real-world problem-solving purposes. In the DE algorithm, few parameters are used, and the population is evolved by applying various operations. It is difficult in evolutionary computation algorithms to maintain population diversity. The main issue is the sub-population of the DE algorithm that helps improve convergence speed and escape from the local optimum. Evolving sub-populations by maintaining diversity is an important issue in the literature that is considered in this research. A solution is proposed that uses sub-populations to promote greater diversity within the population and improve the algorithm performance. DE, heterogeneous distributed differential evolution (HDDE), multi-population ensemble differential evolution (MPEDE), and the proposed improved multi-population ensemble differential evolution (IMPEDE) are implemented using parameter settings; population sizes of 100 NP, 150 NP, and 200 NP; and dimensions of 10D, 30D, and 50D for performance comparison. Different combinations of mutations are used to generate the simulated results. The simulation results are generated using 1000, 3000, and 5000 iterations. Experimental outcomes show the superior results of the proposed IMPEDE over existing algorithms. The non-parametric significance Friedman test confirms that there is a significant difference in the performance of the proposed algorithm and other algorithms used in this study by considering a 0.05 level of significance using six benchmark functions.

An Enhanced Hunger Games Search Optimization with Application to Constrained Engineering Optimization Problems.

The Hunger Games Search (HGS) is an innovative optimizer that operates without relying on gradients and utilizes a population-based approach. It draws inspiration from the collaborative foraging activities observed in social animals in their natural habitats. However, despite its notable strengths, HGS is subject to limitations, including inadequate diversity, premature convergence, and susceptibility to local optima. To overcome these challenges, this study introduces two adjusted strategies to enhance the original HGS algorithm. The first adaptive strategy combines the Logarithmic Spiral (LS) technique with Opposition-based Learning (OBL), resulting in the LS-OBL approach. This strategy plays a pivotal role in reducing the search space and maintaining population diversity within HGS, effectively augmenting the algorithm's exploration capabilities. The second adaptive strategy, the dynamic Rosenbrock Method (RM), contributes to HGS by adjusting the search direction and step size. This adjustment enables HGS to escape from suboptimal solutions and enhances its convergence accuracy. Combined, these two strategies form the improved algorithm proposed in this study, referred to as RLHGS. To assess the efficacy of the introduced strategies, specific experiments are designed to evaluate the impact of LS-OBL and RM on enhancing HGS performance. The experimental results unequivocally demonstrate that integrating these two strategies significantly enhances the capabilities of HGS. Furthermore, RLHGS is compared against eight state-of-the-art algorithms using 23 well-established benchmark functions and the CEC2020 test suite. The experimental results consistently indicate that RLHGS outperforms the other algorithms, securing the top rank in both test suites. This compelling evidence substantiates the superior functionality and performance of RLHGS compared to its counterparts. Moreover, RLHGS is applied to address four constrained real-world engineering optimization problems. The final results underscore the effectiveness of RLHGS in tackling such problems, further supporting its value as an efficient optimization method.

Shuffled ARSH-FATI: A Novel Meta-Heuristic for Lifetime Maximization of Range-Adjustable Wireless Sensor Networks

Wireless Sensor Network (WSN) is an essential component of the Internet-of-Things (IoT), enabling the creation of a vast network of interconnected devices and systems that can interact with each other and with the physical world. WSNs are widely used for a variety of applications, including environmental monitoring, healthcare, industry 4.0, agriculture, transportation, and surveillance. WSN consists of devices with limited residual energy therefore, the lifetime improvement of WSN is critical for achieving the full potential of IoT and enabling the creation of more robust and reliable connected systems. This paper explores the problem of Lifetime Maximization of Range Adjustable Sensors (LM-RAS). The goal is to optimize the lifetime of the WSN consisting of range-adjustable sensors while satisfying two constraints. Firstly, all targets in the network must be monitored simultaneously. Secondly, the activation time of each sensor should be limited by its battery energy. A novel meta-heuristic called Shuffled ARSH-FATI has been proposed that embeds ARSH-FATI into a multi-population method. Shuffled ARSH-FATI solves the LM-RAS problem by splitting it into two sub-problem. The first sub-problem deals with the creation of energy-efficient coverage schemes and the second sub-problem deal with the schedule of these schemes. Shuffled ARSH-FATI utilizes a Linear Programming (LP) model to generate an optimal schedule for the coverage schemes but its performance greatly depends on the quality of the coverage schemes. Shuffled ARSH-FATI simultaneously explores several regions of the search space by partitioning the coverage schemes in population into several sub-populations. It evolves each sub-population (in this problem a sub-set of coverage schemes) independently using ARSH-FATI. It incorporates the shuffling process via Merge, Shuffle, and Partition (MSP) algorithm that merges the sub-populations, shuffles, and then partitions the merged population into new sub-populations. The shuffling process along with the strategy to fine-tune the coverage scheme into energy-efficient coverage schemes maintains population diversity throughout the optimization. The diverse high-quality coverage scheme improves the performance of the LP model.

Thermal-economic optimization design of shell and tube heat exchanger using an improved sparrow search algorithm

Optimal design of heat exchangers is challenging, since complex non-linear relationship exists among the various design parameters. This study aims to construct a full-dimensional thermal-economic model for shell-and-tube heat exchanger using two commonly (i.e., Kern and Bell-Delaware) methods based on Logarithmic-Mean-Temperature-Difference (LMTD) or ε-Number-of-Transfer-Units (ε-NTU) method and proposes a novel solving mechanism based on improved sparrow search algorithm to achieve thermal-economic optimization design of heat exchanger. The improved method organically combines nonlinear gradient descent weight factor, elite reverse strategy, selection-mutation operator to maintain population diversity, enhance convergence speed and avoid being stuck in local optima. Firstly, the constructed model’s accuracy is verified by numerical analysis. Then, the improved sparrow search algorithm is applied to four examples after algorithm parameter tuning, and the results show that the area decreases by 15.62%∼49.30% and total annual cost is reduced by 27.93% compared to the literature. It can also identify feasible solutions at a lower total cost than the metaheuristic method, with reductions of 7.56%∼59.42%(Kern) and 6.26%∼60.19%(Bell-Delaware). Moreover, a multi-objective optimization algorithm is obtained by adding fast non-dominated sorting mechanism and archival gene pool, and the superiority is verified by standard test functions. Finally, a multi-objective optimization scheme with the higher effectiveness-economic benefit is conducted to achieve cost-effectiveness tradeoff for two real-world cases. The results show that total cost of the most economic-efficient scheme decreases by 53.73% and effectiveness increases by 2.33%, while the runtime decreases by 35%. Furthermore, sensitivity analysis of effectiveness and total cost varying with crucial variables is performed and discussed.

Identification of photovoltaic module parameters by implementing a novel teaching learning based optimization with unique exemplar generation scheme (TLBO-UEGS)

The performance evaluation of a Photovoltaic (PV) system heavily relies on accurately estimating the parameters based on its current—voltage relationships. However, due to the PV model’s inherent complexity, obtaining these parameters with precision and efficiency is a challenging task. In this study, a new variant known as teaching learning-based optimization with unique exemplar generation schemes (TLBO-UEGS) is proposed to address PV module parameter estimation problems with robustness and effectiveness. To enhance the performance of TLBO-UEGS, a modified initialization scheme that leverages the strengths of chaotic maps and dynamic oppositional based learning is introduced. This scheme ensures the generation of an initial population with improved solution quality. Furthermore, both the modified teacher phase and modified learner phase are integrated within the TLBO-UEGS optimization framework. This integration allows for different learning strategies to be employed based on the fitness values of each learner, effectively updating their search trajectories. Within the modified teacher phase, two unique exemplar generation schemes are designed to facilitate more effective guidance for learners in the first half of the population while maintaining population diversity. Meanwhile, the modified learner phase emulates a realistic knowledge acquisition process by enabling learners in the second half of the population to engage in collaborative learning with multiple peer learners or retain valuable knowledge from previous learning processes. Extensive simulations demonstrate that TLBO-UEGS achieves superior results, with the minimum root mean square error (RMSE) values of 3.5644 × 10−04 ± 0.0014, 1.3237 × 10−04± 0.0043, and 6.6016 × 10−06± 0.00011 obtained for Photowatt-PWP201, Leibold Solar (LSM 20), and Leybold Solar (STE 4/100) PV modules, respectively.

An integrated differential evolution of multi-population based on contribution degree

The differential evolution algorithm based on multi-population mainly improves its performance through mutation strategy and grouping mechanism. However, each sub-population plays a different role in different periods of iterative evolution. If each sub-population is assigned the same computing resources, it will waste a lot of computing resources. In order to rationally distribute computational resources, an integrated differential evolution of multi-population based on contribution degree (MDE-ctd) is put forth in this work. In MDE-ctd, the whole population is divided into three sub-populations according to different update strategies: archival, exploratory, and integrated sub-populations. MDE-ctd dynamically adjusts computing resources according to the contribution degree of each sub-population. It can effectively use computing resources and speed up convergence. In the updating process of integrated sub-populations, a mutation strategy pool and two-parameter value pools are used to maintain population diversity. The experimental results of CEC2005 and CEC2014 benchmark functions show that MDE-ctd outperforms other state-of-art differential evolution algorithms based on multi-population, especially when it deals with highly complex optimization problems.Graphical abstractAn integrated differential evolution of multi-population based on contribution degree

A Multi-Objective Optimization Method for a Tractor Driveline Based on the Diversity Preservation Strategy of Gradient Crowding

This study presents a multi-objective optimization method for a tractor driveline based on the diversity maintenance strategy of gradient crowding. The objective was to address the trade-off between high power and low fuel consumption rates in a tractor driveline by optimizing the distribution of driveline ratios, aiming to enhance overall driving performance and reduce fuel consumption. This method introduces a strategy for evaluating gradient crowding to reduce non-inferior solution sets during selection to ensure the uniform and wide distribution of solutions while maintaining population diversity. The transmission ratio of a tractor is optimized by varying the input of the transmission ratios in each gear, constraining the theoretical tractor driving rate, common transmission ratio, and drive adhesion limit, and introducing the diversity maintenance strategy of gradient crowding. The goal is to reduce the loss rate of driving power and specific fuel consumption as much as possible. The analysis results demonstrate that the GC_NSGA-II algorithm, incorporating the evaluation strategy of gradient crowding, achieves greater diversity and a more uniform distribution in the front end. After verifying the algorithm, the optimized tractor showed a reduction of 41.62 (±S.D. 0.44)% in the theoretical loss rate of driving power and 62.8 (±S.D. 0.56)% in the loss rate of specific fuel consumption, indicating that the tractor’s drive performance significantly improved, accompanied by a substantial reduction in the fuel consumption rate. These findings affirm the feasibility of the proposed optimization method and provide valuable research insights for enhancing the overall performance of tractors.