Quality Of Initial Population Research Articles

A Resampling Ant Colony Optimization with Elite Exploration and Convergence Mechanism for Multithreshold Segmentation of Breast Cancer Images

Previous studies have emphasized the potential of threshold image segmentation for early breast cancer detection. However, traditional methods encounter challenges regarding low segmentation efficiency and accuracy. Addressing this, the ant colony optimization algorithm for continuous optimization (ACOR) shows promise. Yet, existing ACOR variants still grapple with poor initial population quality, affecting convergence speed and avoiding local optimization. These issues impact segmentation efficiency and accuracy. To tackle them, this study introduces RESACO, an enhanced ACOR version integrating three novel optimization strategies: resampling initialization (RIS), elite exploration (EES), and strengthened convergence mechanism (SCM). RIS enhances initial population quality by resampling regions with individuals demonstrating superior fitness and segmentation efficiency. EES promotes exploration across the search space, preventing local optima entrapment and enhancing model stability. SCM expediting convergence, segmentation efficiency, and precision. RESACO's performance is assessed through extensive experiments using IEEE CEC 2014 and IEEE CEC 2022 benchmark functions, including ablation experiments and comparisons with basic and improved algorithms and ACOR variants. Subsequently, the threshold image segmentation model based on RESACO is compared with other models using metaheuristic algorithms for segmenting realistic breast cancer medical images. Results demonstrate the proposed model's faster convergence and higher segmentation accuracy, preserving more lesion tissue details.

A multi-objective teaching-learning-based optimizer for a cooperative task allocation problem of weeding robots and spraying drones

In recent years, both intelligent robots and drones have been widely used in agriculture. This paper studies a cooperative task allocation problem of weeding robots and spraying drones (WRSDCTA) with two objectives of minimizing the maximum completion time and minimizing the total residual herbicide. Based on the characteristics of the problem, a mathematical model is established. An effective multi-objective teaching-learning-based optimizer (MOTLBO) is proposed to solve this problem. At first, two heuristic initialization methods are designed to generate high quality initial population. And then, a multi-teacher strategy is presented when selecting teachers and students. For teachers, two heuristic search operators are designed to improve personal ability. For students, an improved OBX crossover operator is designed to learn from selected teachers. In addition, a multi-neighborhood search strategy is presented to further improve the local search capability of the algorithm. Finally, the proposed MOTLBO algorithm is compared with several state-of-the-art algorithms from close-related literature, and the results demonstrate the effectiveness and high performance of MOTLBO for solving the WRSDCTA problem.

Hybrid beluga whale optimization algorithm with multi-strategy for functions and engineering optimization problems

Beluga Whale Optimization (BWO) is a new metaheuristic algorithm that simulates the social behaviors of beluga whales swimming, foraging, and whale falling. Compared with other optimization algorithms, BWO shows certain advantages in solving unimodal and multimodal optimization problems. However, the convergence speed and optimization performance of BWO still have some performance deficiencies when solving complex multidimensional problems. Therefore, this paper proposes a hybrid BWO method called HBWO combining Quasi-oppositional based learning (QOBL), adaptive and spiral predation strategy, and Nelder-Mead simplex search method (NM). Firstly, in the initialization phase, the QOBL strategy is introduced. This strategy reconstructs the initial spatial position of the population by pairwise comparisons to obtain a more prosperous and higher quality initial population. Subsequently, an adaptive and spiral predation strategy is designed in the exploration and exploitation phases. The strategy first learns the optimal individual positions in some dimensions through adaptive learning to avoid the loss of local optimality. At the same time, a spiral movement method motivated by a cosine factor is introduced to maintain some balance between exploration and exploitation. Finally, the NM simplex search method is added. It corrects individual positions through multiple scaling methods to improve the optimal search speed more accurately and efficiently. The performance of HBWO is verified utilizing the CEC2017 and CEC2019 test functions. Meanwhile, the superiority of HBWO is verified by utilizing six engineering design examples. The experimental results show that HBWO has higher feasibility and effectiveness in solving practical problems than BWO and other optimization methods.

Optimal scheduling strategy of electric vehicle based on improved NSGA-III algorithm.

Aiming at the problem of load increase in distribution network and low satisfaction of vehicle owners caused by disorderly charging of electric vehicles, an optimal scheduling model of electric vehicles considering the comprehensive satisfaction of vehicle owners is proposed. In this model, the dynamic electricity price and charging and discharging state of electric vehicles are taken as decision variables, and the income of electric vehicle charging stations, the comprehensive satisfaction of vehicle owners considering economic benefits and the load fluctuation of electric vehicles are taken as optimization objectives. The improved NSGA-III algorithm (DJM-NSGA-III) based on dynamic opposition-based learning strategy, Jaya algorithm and Manhattan distance is used to solve the problems of low initial population quality, easy to fall into local optimal solution and ignoring potential optimal solution when NSGA-III algorithm is used to solve the multi-objective and high-dimensional scheduling model. The experimental results show that the proposed method can improve the owner's satisfaction while improving the income of the charging station, effectively alleviate the conflict of interest between the two, and maintain the safe and stable operation of the distribution network.

Fog Computing Task Scheduling of Smart Community Based on Hybrid Ant Lion Optimizer

Due to the problem of large latency and energy consumption of fog computing in smart community applications, the fog computing task-scheduling method based on Hybrid Ant Lion Optimizer (HALO) is proposed in this paper. This method is based on the Ant Lion Optimizer (ALO. Firstly, chaotic mapping is adopted to initialize the population, and the quality of the initial population is improved; secondly, the Adaptive Random Wandering (ARW) method is designed to improve the solution efficiency; finally, the improved Dynamic Opposite Learning Crossover (DOLC) strategy is embedded in the generation-hopping stage of the ALO to enrich the diversity of the population and improve the optimization-seeking ability of ALO. HALO is used to optimize the scheduling scheme of fog computing tasks. The simulation experiments are conducted under different data task volumes, compared with several other task scheduling algorithms such as the original algorithm of ALO, Genetic Algorithm (GA), Whale Optimizer Algorithm (WOA) and Salp Swarm Algorithm (SSA). HALO has good initial population quality, fast convergence speed, and high optimization-seeking accuracy. The scheduling scheme obtained by the proposed method in this paper can effectively reduce the latency of the system and reduce the energy consumption of the system.

A Multi-Objective Local Optimization Method for Imaging Optical Systems

Addressing the inherent limitations of conventional local optimization methodologies such as damping least squares (DLS) and adaptation algorithms, this study proposes a novel approach to multi-objective optimization for imaging optical systems. The proposed method entails the formulation of a multi-objective optimization mathematical framework, where the objectives are established upon lateral aberration and wave aberration criteria. Subsequently, enhancements are made to the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) by implementing a directional initial population strategy and parallel optimization with multiple-trajectory planning. The genesis of the initial population is rooted in the gradient direction information extracted from the starting positions. This strategic foundation precludes potential efficiency limitations in subsequent optimization stages arising from undesirable initial population quality. By employing mechanisms such as differentiation and mutation to sustain population diversity, the trajectory of evolution is guided by the first- and second-order derivatives of the optimal individual, thereby elevating the quality of the evolutionary offspring population. The fusion of the parent and offspring populations yields a composite population, which undergoes rapid and non-dominated sorting, crowding calculation, and elite strategies. Empirical results illustrate the validation of the proposed methodology. The proof-of-concept paradigm demonstrates high efficiency in multi-objective local optimization for imaging optical systems.

IYDSE: Ameliorated Young’s double-slit experiment optimizer for applied mechanics and engineering

The Young’s Double-Slit Experiment (YDSE) optimizer is a newly developed swarm intelligence algorithm inspired by Young’s double-slit experiment that reveals the wave nature of light. Due to its simple structure and efficiency, the researchers employed the algorithm to address real-world optimization problems in multiple domains. However, in large-scale mechanical and engineering applications, it still needs to balance exploration and exploitation effectively and easily fall into local optimum. Therefore, in order to ameliorate these shortcomings, a multi-strategy improved YDSE optimizer named IYDSE is developed in this study. In IYDSE, introducing the Bernoulli map strategy ensures a higher quality initial population in the initialization, thus improving the convergence speed. Then, Levy flight is utilized as a local development engine to avoid local search stagnation of the optimal solution during optimization. In addition, mirror reflection learning is designed to enhance group diversity. Finally, the information entanglement strategy is introduced to enhance the information exchange between light and dark fringes, thus balancing the exploration-development capability. The superiority of the proposed IYDSE is comprehensively demonstrated by comparing it with state-of-the-art algorithms on the CEC2019 and CEC2020 test suites, respectively. Meanwhile, the practicability of IYDSE is also verified by solving five specific engineering design cases and 50 different types of engineering optimization suites. Finally, the IYDSE algorithm is applied to three truss topology optimization problems. The experimental results demonstrate the applicability and development potential of the proposed algorithm in practical engineering applications. Therefore, IYDSE is potentially an effective and competitive algorithm for solving applied mechanics and engineering.

Research on Transformer Fault Diagnosis Model Based on Improved GOA-SVM

A transformer fault diagnosis model based on the improved grasshopper optimization algorithm-optimized support vector machine (SVM) is proposed to improve the precision of transformer fault diagnosis and avoid the issues that the traditional grasshopper optimization algorithm (GOA) is prone to falling into local optimal solution and slow convergence. Firstly, the grasshopper population is initialized using the elite backward learning strategy to improve the initial population quality and search efficiency. Then, the Sigmoid function is introduced to improve the linear weight decreasing of the grasshopper algorithm into nonlinear weight decreasing to strike a balance between the algorithm’s capacity for both local and global exploration. Finally, the kernel function parameters and penalty coefficients of the SVM are optimized with the improved grasshopper optimization algorithm (IGOA) to establish a model based on dissolved gas analysis (DGA) in oil-based IGOA algorithm-optimized SVM for transformer fault diagnosis model and verify the effectiveness and superiority of IGOA-SVM to identify transformer fault states by comparing with PSO-SVM and GOA-SVM.

An Adaptive Sinusoidal-Disturbance-Strategy Sparrow Search Algorithm and Its Application.

In light of the problems of slow convergence speed, insufficient optimization accuracy and easy falling into local optima in the sparrow search algorithm, this paper proposes an adaptive sinusoidal-disturbance-strategy sparrow search algorithm (ASDSSA) and its mathematical equation. Firstly, the initial population quality of the algorithm is improved by fusing cubic chaos mapping and perturbation compensation factors; secondly, the sinusoidal-disturbance-strategy is introduced to update the mathematical equation of the discoverer's position to improve the information exchange ability of the population and the global search performance of the algorithm; finally, the adaptive Cauchy mutation strategy is used to improve the ability of the algorithm to jump out of the local optimal solutions. Through the optimization experiments on eight benchmark functions and CEC2017 test functions, as well as the Wilcoxon rank-sum test and time complexity analysis, the results show that the improved algorithm has better optimization performance and convergence efficiency. Further, the improved algorithm was applied to optimize the parameters of the long short term memory network (LSTM) model for passenger flow prediction on selected metro passenger flow datasets. The effectiveness and feasibility of the improved algorithm were verified by experiments.

Bi-objective service composition and optimal selection for cloud manufacturing with QoS and robustness criteria

As an important part of cloud manufacturing, service composition and optimal selection (SCOS) has attracted many scholars’ attentions. Although there are a large number of researches devoted to this field, plenty of challenges still remain, such as the difficulties of coping with real-life uncertainties and improving efficiency in making decisions. To this end, a bi-objective service composition and optimal selection (BoSCOS) problem which takes both QoS and robustness into account is firstly studied in this paper, its mathematical model is established after the two criteria are constructed. Then, a strengthened multi-objective gray wolf optimizer (SMOGWO) is developed to solve the above model with high efficiency, in which three improvement strategies are introduced to the original MOGWO: (1) the opposition based learning (OBL) strategy is adopted to improve the quality of initial population; (2) a leader update strategy is designed to explore better search leaders and balance the exploitation and exploration abilities; (3) an improved archiving strategy is developed to retain all possible dominant individuals, so as to improve the population diversity based on the Pareto dominance. Finally, the effectiveness of the proposed algorithm is verified on 8 BoSCOS problems with different scales and 17 benchmark functions, results show that the improvement strategies are effective and SMOGWO is superior to its competitors in terms of convergence and population diversity.

DEPICTER: A Design-Principle Guided and Heuristic-Rule Constrained Software Refactoring Approach

Software refactoring is one of the most significant practices in software maintenance as the quality of software design tends to deteriorate during software evolution. But, refactoring software is a very challenging task as it requires a holistic view of the entire software system. To this end, recent studies introduced search-based algorithms to facilitate software refactoring. However, they still have the following major limitations: 1) the searched solutions may violate the design principles as their fitness functions do not directly reflect the degree of software’s compliance with design principles; 2) most approaches start the searching process from a completely random initial population, which may lead to unoptimal solutions. In this article, we aim to develop effective search-based refactoring approach to recommend better refactoring activities for developers which can improve the degree of software’s compliance with design principles as well as the software design quality. We propose <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DEPICTER</monospace> , a design-principle guided and heuristic-rule constrained software refactoring recommendation approach. In particular, <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DEPICTER</monospace> uses non-dominated sorting genetic algorithm (NSGA)-II genetic algorithm and employs design-principle metrics as fitness functions. Besides, <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DEPICTER</monospace> leverages heuristic rules to improve the quality of initial population for subsequent generic evolution. Our evaluations, based on four widely used systems, show that <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DEPICTER</monospace> is effective for guiding the development of better refactoring models in practice.

Directional mutation and crossover for immature performance of whale algorithm with application to engineering optimization

AbstractIn recent years, a range of novel and pseudonovel optimization algorithms has been proposed for solving engineering problems. Swarm intelligence optimization algorithms (SIAs) have become popular methods, and the whale optimization algorithm (WOA) is one of the highly discussed SIAs. However, regardless of novelty concerns about this method, the basic WOA is a weak method compared to top differential evolutions and particle swarm variants, and it suffers from the problem of poor initial population quality and slow convergence speed. Accordingly, in this paper, to increase the diversity of WOA versions and enhance the performance of WOA, a new WOA variant, named LXMWOA, is proposed, and based on the Lévy initialization strategy, the directional crossover mechanism, and the directional mutation mechanism. Specifically, the introduction of the Lévy initialization strategy allows initial populations to be dynamically distributed in the search space and enhances the global search capability of the WOA. Meanwhile, the directional crossover mechanism and the directional mutation mechanism can improve the local exploitation capability of the WOA. To evaluate its performance, using a series of functions and three models of engineering optimization problems, the LXMWOA was compared with a broad array of competitive optimizers. The experimental results demonstrate that the LXMWOA is significantly superior to its exploration and exploitation capability peers. Therefore, the proposed LXMWOA has great potential to be used for solving engineering problems.

Research on Job-shop Scheduling Problem Based-on Improved Genetic Algorithm

Considering minimizing completion time and maximizing equipment utilization of job shop scheduling problem, an improved genetic algorithm was proposed. First, constructing a two-layer chromosome coding structure, with which the initial population quality and the diversity of the population were strengthened and improved. Next, in order to enhance the local search ability and improve the algorithm convergence speed, the population generated after selection, crossover, and mutation was divided into families, and the elite population were combined with explosive fireworks of the firework algorithm. Then, in order to prevent the algorithm from maturing prematurely, the disadvantaged groups were mutated through(by) the dynamic mutation mechanism to narrow the gap between the super individual and the average. Last, the parameters of the algorithm were optimized by orthogonal tests, and performance of the algorithm was verified by classical test examples.

Parameter identification of photovoltaic cell model based on improved grasshopper optimization algorithm

An accurate mathematical model has practical applications in the design and research of solar cells. Many intelligent optimization algorithms are currently used to extract solar cell parameters. However, optimization algorithms including grasshopper optimization algorithm (GOA) are easy to fall into local optimums. An improved grasshopper optimization algorithm (IGOA) was proposed in this paper. Chaotic initialization was carried out to improve the quality of initial population. Then differential evolution strategy was introduced to maintain the diversity of the population through mutation, crossover and selection process, leading to getting rid of local optimums and searching for better solutions in GOA. To accelerate the convergence speed of the algorithm, the positions of particles were updated with current particle optimal values as targets as in particle swarm optimization. The optimization experiments on standard test functions shown the superiority of IGOA compared with other intelligent optimization algorithms. IGOA was then used to identify the parameters of polycrystalline silicon solar cells. The identification accuracy and stability of IGOA are much higher than harris hawks optimization, grey wolf optimization and ant lion optimization. The effectiveness of IGOA in identifying parameters of solar cell under different illuminations was also verified by experiments.

An efficient particle swarm optimization with homotopy strategy for global numerical optimization

Particle swarm optimization (PSO) as a successful optimization algorithm is widely used in many practical applications due to its advantages in fast convergence speed and convenient implementation. As a population optimization algorithm, the quality of initial population plays an important role in the performance of PSO. However, random initialization is used in population initialization for PSO. Using the solution of the solved problem as prior knowledge will help to improve the quality of the initial population solution. In this paper, we use homotopy analysis method (HAM) to build a bridge between the solved problems and the problems to be solved. Therefore, an improved PSO framework based on HAM, called HAM-PSO, is proposed. The framework of HAM-PSO includes four main processes. It contains obtaining the prior knowledge, constructing homotopy function, generating initial solution and solving the to be solved by PSO. In fact, the framework does not change the PSO, but replaces the random population initialization. The basic PSO algorithm and three others typical PSO algorithms are used to verify the feasibility and effectiveness of this framework. The experimental results show that the four PSO using this framework are better than those without this framework.

Locust swarm optimisation for the permutation flow shop scheduling problem

The permutation flow shop scheduling problem (PFSP) is one of the most prominent types of combinatorial optimisation problems. The fact comes from its wide applications in manufactures and industries. In this paper, the locust swarm optimisation algorithm (LO) is proposed to find high quality solution for the PFSP, it simulates the behaviour of locust swarm. In this work, the LO algorithm is adapted to solve the PFSP with the objective of minimising the makespan where a heuristic rule called smallest position value (SPV) is triggered to transform the random numbers into sequences of jobs. Then the NEH heuristic of Nawaz-Enscore-Ham is applied to generate good quality initial population. Also, a simple and efficient iterated local search method is employed to explore the solution space efficiently. An experimental study is discussed in this paper to evaluate the performance of the introduced algorithm. Different well-known PFSP benchmarks of small, medium and large size instances are used for this purpose. The computational and statistical studies demonstrate that the proposed algorithm is evidently outperforming the recent algorithms in obtaining better solutions.

Illumination correction via support vector regression based on improved whale optimization algorithm

AbstractVariations in illumination lead to serious errors in the evaluation of chromatic aberration. We propose an illumination correction model based on the opposition‐based learning improved whale optimization algorithm for support vector regression optimization, named "OBL‐IWOA‐SVR." Because the initial population quality of the whale optimization algorithm has a significant impact on the solution speed and accuracy, the Opposition‐based learning strategy is adopted in this article to mix the original population and its opposite individuals and select the best as the new population, replacing the random initialization to generate a more suitable initial population. This increases the diversity of the population and thus overcomes the impact of the quality of the initial population on the algorithm performance. Secondly, the proposed algorithm adopts the adaptive weight and the convergence factor based on the variation of the cosine law to balance the algorithm's global exploration ability and local development ability and enhance the convergence accuracy. Finally, the algorithm utilizes good global searching ability to optimize the penalty factor and nuclear parameters and obtains the optimal support vector machine parameter combination to construct the illumination correction model OBL‐IWOA‐SVR accurately and robustly. Experimental results show that the illumination correction model proposed in this article is found superior to other models in significance analysis: the root mean square error of the proposed model is 0.0173, the smallest of all illumination correction models. Furthermore, the model exhibits good stability and high illumination estimation accuracy.

Power forecasting-based coordination dispatch of PV power generation and electric vehicles charging in microgrid

We propose herein an extended power forecasting-based coordination dispatch method for PV power generation microgrid with plug-in EVs (PVEVM) to improve the local consumption of renewable energy in the microgrid by guiding electric vehicle (EV) orderly charging. In this method, we use a clustering algorithm and neural network to build a power forecasting model (PFM) based on real data which can effectively characterise the uncertainty of PV power generation and EV charging load. Based on the interaction between the energy control centre (ECC) of the PVEVM and the EV users, a one-leader multiple-follower Stackelberg game is formulated, and the Stackelberg equilibrium is determined by using a power forecasting-based genetic algorithm (GA). As a main contribution of this paper, the PV power generation and EV charging load output from the PFM are used to generate a better quality initial population of the GA to improve its performance. A case study using real data from the Aifeisheng PV power station in China and EV charging stations in the UK verifies the good performance of the proposed extended coordination dispatch algorithm.

Intelligent Mapping Method for Power Consumption and Delay Optimization Based on Heterogeneous NoC Platform

As integrated circuit processes become more advanced, feature sizes become smaller and smaller, and more and more processing cores and memory components are integrated on a single chip. However, the traditional bus-based System-on-Chip (SoC) communication is inefficient, has poor scalability, and cannot handle the communication tasks between the processing cores well. Network-on-chip (NoC) has become an important development direction in this field by virtue of its efficient transmission and scalability of data between multiple cores. The mapping problem is a hot spot in NoC's research field, and its mapping results will directly affect the power consumption, latency, and other properties of the chip. The mapping problem is a NP-hard problem, so how to effectively obtain low-power and low-latency mapping schemes becomes a research difficulty. Aiming at this problem, this paper proposes a two-populations-with-enhanced-initial-population based on genetic algorithm (TI_GA) task mapping algorithm based on an improved genetic algorithm from the two indexes of power consumption and delay. The quality of the initial individual is optimized in the process of constructing the population, so the quality of initial population is improved. In addition, a two-population genetic mechanism is added during the iterative process of the algorithm. The experimental results show that TI_GA is very effective for optimizing network power consumption and delay of heterogeneous multi-core.

DI_GA: A Heuristic Mapping Algorithm for Heterogeneous Network-on-Chip

Heterogeneous multi-core processors have become the forefront of processor development due to their advantages in system throughput and execution efficiency, but they also bring many new challenges to system design. The mapping of heterogeneous Network-on-Chip (NoC) is challenging. To solve this problem, this paper proposes a heterogeneous multi-core processor task mapping algorithm based on an improved genetic algorithm. By constructing a good initial population method to improve the initial population quality, a dual population genetic mechanism is used in the iteration process. The algorithm can make tasks more reasonably distributed to various network nodes, and has high efficiency for optimizing network power consumption on heterogeneous multi-cores.