Opposition-based Learning Strategy Research Articles

Parameter Identification of Photovoltaic Models Using Enhanced Crayfish Optimization Algorithm with Opposition-Based Learning Strategies

Parameter Identification of Photovoltaic Models Using Enhanced Crayfish Optimization Algorithm with Opposition-Based Learning Strategies

Free vibration analysis and multi-objective robust optimization of three-dimensional pyramidal truss core sandwich plates with interval uncertain parameters

In this study, the free vibration analysis and multi-objective robust optimization of three-dimensional pyramidal truss core sandwich plates with interval uncertain parameters are fulfilled. The numerical model for free vibration of the plate is derived by combining the three-dimensional elasticity theory and Rayleigh-Ritz method, and the validity of the model is illustrated by numerical results. On this basis, considering various uncertainties within the plate, a new uncertainty-propagation analysis method is constructed by integrating the numerical model, interval-analysis model, kriging model and optimization. The one-dimensional and multi-source uncertainty-propagation analysis of the fundamental frequency is finished using this method, and the accuracy is proved by comparing with Monte Carlo simulation results. Meanwhile, to minimize the effects of uncertainties on the performance of plates at the design stage, an objective multi-objective robust optimization model with the objectives of maximizing the fundamental frequency and minimizing the robustness factor is established. Finally, an improved pelican optimization algorithm is proposed by introducing the improved opposition-based learning strategy, tracking strategy with step control and convergence strategy. And the Pareto front and corresponding design schemes applicable to different working environments are obtained without repeating the optimization.

Implementation of an Enhanced Crayfish Optimization Algorithm.

This paper presents an enhanced crayfish optimization algorithm (ECOA). The ECOA includes four improvement strategies. Firstly, the Halton sequence was used to improve the population initialization of the crayfish optimization algorithm. Furthermore, the quasi opposition-based learning strategy is introduced to generate the opposite solution of the population, increasing the algorithm's searching ability. Thirdly, the elite factor guides the predation stage to avoid blindness in this stage. Finally, the fish aggregation device effect is introduced to increase the ability of the algorithm to jump out of the local optimal. This paper performed tests on the widely used IEEE CEC2019 test function set to verify the validity of the proposed ECOA method. The experimental results show that the proposed ECOA has a faster convergence speed, greater performance stability, and a stronger ability to jump out of local optimal compared with other popular algorithms. Finally, the ECOA was applied to two real-world engineering optimization problems, verifying its ability to solve practical optimization problems and its superiority compared to other algorithms.

An improved slime mould algorithm using multiple strategies

Aiming at the defects of standard slime mould algorithm (SMA), such as local optima stagnation, slow convergence and improper balance between exploitation and exploration, we propose an improved SMA that contains the adaptive t-distributed variation strategy, improved location update formula and chaotic opposition-based learning strategy, that is, the MISMA. Utilizing comparative experiments and ablation studies on classical benchmark and CEC2020 benchmark suite, we proved that MISMA outperforms other state-of-the-art rival algorithms on convergence speed, solution accuracy, and robustness, each component of MISMA achieves an improvement on the defects of SMA at each stage and exhibits synergistic effects.

An Improved Dung Beetle Optimization Algorithm for High-Dimension Optimization and Its Engineering Applications

One of the limitations of the dung beetle optimization (DBO) is its susceptibility to local optima and its relatively low search accuracy. Several strategies have been utilized to improve the diversity, search precision, and outcomes of the DBO. However, the equilibrium between exploration and exploitation has not been achieved optimally. This paper presents a novel algorithm called the ODBO, which incorporates cat map and an opposition-based learning strategy, which is based on symmetry theory. In addition, in order to enhance the performance of the dung ball rolling phase, this paper combines the global search strategy of the osprey optimization algorithm with the position update strategy of the DBO. Additionally, we enhance the population’s diversity during the foraging phase of the DBO by incorporating vertical and horizontal crossover of individuals. This introduction of asymmetry in the crossover operation increases the exploration capability of the algorithm, allowing it to effectively escape local optima and facilitate global search.

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.

Chaotic opposition Golden Sinus Algorithm for global optimization problems

Optimization techniques are required to find the best solutions to challenging problems in many engineering disciplines. Metaheuristic algorithms that effectively increase search performance using various evolutionary strategies have become increasingly popular in recent years. The Golden Sinus Algorithm (GoldSa) is a population-based optimization algorithm that uses the sine function and the golden ratio. In this study, a chaotically enhanced opposition-based Golden Sinus Algorithm (Co-GoldSa) has been proposed to improve the efficiency of the exploitation and exploration ability of the GoldSa method. While designing this approach, it is first necessary to analyze the chaotic behavior of the GoldSa parameters. For this purpose, three chaotic GoldSa methods have been developed using eight chaotic maps to determine the effect of the chaotic maps on the parameters with different behaviors. Secondly, the opposition-based learning strategy is adjusted to the cGoldSa to enhance the searching ability. To investigate the proficiency of the proposed Co-GoldSa method, it has been examined with well-known and newly introduced metaheuristic approaches on benchmark functions and classical engineering design problems. Besides, an efficient framework of the multilevel thresholding image segmentation has been presented based on the Co-GoldSa method since the efficient processing of pathological images is quite important in medical diagnostics. The experimental outcomes reveal the superiority of the proposed method in solving global optimization problems, image segmentation, and engineering problems. Thus, the outcomes of the benchmark functions, image segmentation, and classical engineering problems support that the proposed Co-GoldSa approach can be considered a promising method for resolving challenging optimization problems.

TBM tunneling strata automatic identification and working conditions decision support

It has become an important research topic to ensure the safe and efficient tunnel boring machine (TBM) tunneling in the construction of long distance and deep buried tunnels. This study aims to construct an assistant decision support system that integrates surrounding rock classification identification and tunneling parameters prediction optimization, providing quantitative decision guidance for driving TBM for main drivers. For this purpose, this study takes a TBM diversion tunnel project in Xinjiang as the research background, collects 8633 m length TBM on-site tunneling data, and constructs a TBM tunneling data sample database; The DBSCAN (Density-Based Spatial Clustering of Applications with Noise) clustering algorithm was improved using kernel density estimation to achieve TBM tunnel surrounding rock classification based on tunneling performance and automatic identification. Compared with the traditional DBSCAN model, the Sk parameter average decreased by 30.0%, and the Ku parameter average increased by 181.9%; Adopting the opposition-based learning strategy and β-chaotic sequence method improved the MOGWO (multi-objective grey wolf optimization) algorithm to achieve optimal decision-making of TBM tunneling parameters under 12 working conditions. Compared to the traditional MOGWO model, the Pareto optimality solved by the improved MOGWO model increasing the improvement efficiency of the original value and sample mean by 14.82% and 11.46%, respectively. The on-site application results indicate that the assistant decision support system proposed in this study can provide construction guidance for TBM main drivers under different working conditions, improving the tunneling efficiency and safety of TBM.

Classification of high-dimensional imbalanced biomedical data based on spectral clustering SMOTE and marine predators algorithm

The research of biomedical data is crucial for disease diagnosis, health management, and medicine development. However, biomedical data are usually characterized by high dimensionality and class imbalance, which increase computational cost and affect the classification performance of minority class, making accurate classification difficult. In this paper, we propose a biomedical data classification method based on feature selection and data resampling. First, use the minimal-redundancy maximal-relevance (mRMR) method to select biomedical data features, reduce the feature dimension, reduce the computational cost, and improve the generalization ability; then, a new SMOTE oversampling method (Spectral-SMOTE) is proposed, which solves the noise sensitivity problem of SMOTE by an improved spectral clustering method; finally, the marine predators algorithm is improved using piecewise linear chaotic maps and random opposition-based learning strategy to improve the algorithm’s optimization seeking ability and convergence speed, and the key parameters of the spectral-SMOTE are optimized using the improved marine predators algorithm, which effectively improves the performance of the over-sampling approach. In this paper, five real biomedical datasets are selected to test and evaluate the proposed method using four classifiers, and three evaluation metrics are used to compare with seven data resampling methods. The experimental results show that the method effectively improves the classification performance of biomedical data. Statistical test results also show that the proposed PRMPA-Spectral-SMOTE method outperforms other data resampling methods.

Hybrid Strategy Improved Beetle Antennae Search Algorithm and Application

The multi-dimensional optimization of mechanisms is a typical optimization problem encountered in mechanical design. Herein, the Hybrid strategy improved Beetle Antennae Search (HSBAS) algorithm is proposed to solve the multi-dimensional optimization problems encountered in structural design. To solve the problems of local optimization and low accuracy of the high-dimensional solution of the Beetle Antennae Search (BAS) algorithm, the algorithm adopts the adaptive step strategy, multi-directional exploration strategy, and Lens Opposition-Based Learning strategy, significantly reducing the probability of the algorithm falling into the local optimum and improving its global search capability. Comparative experiments of the improved algorithm are carried out by selecting eleven benchmark test functions. HSBAS can reach 1 × 10−22 accuracy from the optimal value when dealing with low-dimensional functions. It can also obtain 1 × 10−2 accuracy when dealing with high-dimensional functions, significantly improving the algorithm’s capability. According to Friedman’s ranking test result, HSBAS ranks first, which proves that HSBAS is superior to the other three algorithms. The HSBAS algorithm is further used to optimize the design of the altitude compensation module of the gravity compensation device for solar wings, controlling the fluctuation of bearing capacity within 0.25%, which shows that the algorithm can be used as an effective tool for engineering structural optimization problems.

Modified Opposition-Based Particle Swarm Optimization for Combined Economic and Emission Dispatch Problem

Modification of the particle swarm optimization (PSO) method is proposed with an opposition-based learning strategy to find the optimal solution to electrical power dispatch problems. The objective of the proposed algorithm is to address the combined economic and emissions dispatch problem (CEED) of thermal power plants. This problem includes constraints such as the valve point effect, prohibited zones of operation, and ramp rate limits. In order to assess its performance, the proposed algorithm is first evaluated using a set of benchmark functions. Later, three thermal generating systems having 6, 10, and 40 units respectively are regarded as the test systems to validate the proposed method. The proposed method is tested, and a comparison of the results are made with popular optimization techniques reported in the literature such as PDE, MODE, NSGA II, and MOSSA. Promising results have been obtained with opposition-based PSO in comparison with their current equivalents. A comparison was made between the fuel cost, emissions, and CPU time of the proposed method with the two other PSO variants: inertia factor PSO (IFPSO) and constriction factor PSO (CFPSO). The results showed a decline in the overall cost by approximately 3.73% and a decrease in CPU time by as much as 2.6 s. Furthermore, the obtained predictions consistently exhibit a high level of accuracy, typically approaching 100%.

Three-stage multi-modal multi-objective differential evolution algorithm for vehicle routing problem with time windows

In this paper, the mathematical model of Vehicle Routing Problem with Time Windows (VRPTW) is established based on the directed graph, and a 3-stage multi-modal multi-objective differential evolution algorithm (3S-MMDEA) is proposed. In the first stage, in order to expand the range of individuals to be selected, a generalized opposition-based learning (GOBL) strategy is used to generate a reverse population. In the second stage, a search strategy of reachable distribution area is proposed, which divides the population with the selected individual as the center point to improve the convergence of the solution set. In the third stage, an improved individual variation strategy is proposed to legalize the mutant individuals, so that the individual after variation still falls within the range of the population, further improving the diversity of individuals to ensure the diversity of the solution set. Based on the synergy of the above three stages of strategies, the diversity of individuals is ensured, so as to improve the diversity of solution sets, and multiple equivalent optimal paths are obtained to meet the planning needs of different decision-makers. Finally, the performance of the proposed method is evaluated on the standard benchmark datasets of the problem. The experimental results show that the proposed 3S-MMDEA can improve the efficiency of logistics distribution and obtain multiple equivalent optimal paths. The method achieves good performance, superior to the most advanced VRPTW solution methods, and has great potential in practical projects.

MSAO: A multi-strategy boosted snow ablation optimizer for global optimization and real-world engineering applications

Snow Ablation Optimizer (SAO) is a cutting-edge nature-inspired meta-heuristic technique that mimics the sublimation and melting processes of snow in its quest for optimal solution to complex problems. While SAO has demonstrated competitive performance in comparison to classical algorithms in early research, it still exhibits certain limitations including low convergence accuracy, a lack of population diversity, and premature convergence, particularly when addressing high-dimensional intricate challenges. To mitigate the above-mentioned adverse factors, this paper introduces a novel variant of SAO with featuring four enhancement strategies collectively referred as MSAO. Firstly, the good point set initialization strategy is employed to generate a uniformly distributed high-quality population, which facilitates the algorithm to enter the appropriate search domain rapidly. Secondly, the greedy selection method is adopted to reserve better candidate solutions for the next iteration, thus striking a robust exploration–exploitation balance. Then, the Differential Evolution (DE) scheme is introduced to expand the search range and enhance the exploitation capability of the algorithm for higher convergence accuracy. Finally, to reduce the risk of falling into local optima, a Dynamic Lens Opposition-Based Learning (DLOBL) strategy is developed to operate on the current optimal solution dimension by dimension. With the blessing of these strategies, the optimization performance of MSAO is comprehensively improved. To comprehensively evaluate the optimization performance of MSAO, a series of numerical optimization experiments are conducted using the IEEE CEC2017 & CEC2022 test sets. In the IEEE CEC2017 experiments, the optimal crossover probability CR=0.8 is determined and the effectiveness of each improvement strategy is ablatively verified. MSAO is compared with the basic SAO, various state-of-the-art optimizers, and CEC2017 champion algorithms in terms of solution accuracy, convergence speed, robustness, and scalability. In the IEEE CEC2022 experiments, MSAO is compared with some recently developed improved algorithms to further validate its superiority. The results demonstrate that MSAO has excellent overall optimization performance, with the smallest Friedman mean rankings of 1.66 and 1.25 on both test suites, respectively. In the majority of test cases, MSAO can provide more accurate and reliable solutions than other competitors. Furthermore, six realistic constrained engineering design challenges and one photovoltaic model parameter estimation issue are employed to demonstrate the practicality of MSAO. Our findings suggest that MSAO has excellent optimization capacity and broad application potential.

Enhanced multi-strategy bottlenose dolphin optimizer for UAVs path planning

The path planning of unmanned aerial vehicle is a complex practical optimization problem, which is an important part of unmanned aerial vehicle technology. For constrained path planning problem, the traditional path planning methods can not deal with the complex constraint conditions well, and the classical nature-inspired algorithms will find the local optimal solution due to the lack of optimization ability. In this paper, an enhanced multi-strategy bottlenose dolphin optimizer is proposed to solve the unmanned aerial vehicle path planning problem under threat environments. Firstly, the introduction of fish aggregating device strategy that simulates the living habits of sharks enriches the behavioral diversity of the population. Secondly, random mixed mutation strategy and chaotic opposition-based learning strategy expand the exploration range of the algorithm in the solution space by disturbing the positions of some individuals and generating the opposite population respectively. Finally, after balancing the exploration and exploitation ability of the algorithm more reasonably through the mutation factor and energy factor, this paper proposes a new swarm intelligence algorithm. After verifying the adaptability and efficiency of the proposed algorithm through different types of test functions, this paper further highlights the advantages of the proposed algorithm in finding the optimal feasible path in the unmanned aerial vehicle path planning model based on four constraints.

Multi-objective liver cancer algorithm: A novel algorithm for solving engineering design problems

This research introduces the Multi-Objective Liver Cancer Algorithm (MOLCA), a novel approach inspired by the growth and proliferation patterns of liver tumors. MOLCA emulates the evolutionary tendencies of liver tumors, leveraging their expansion dynamics as a model for solving multi-objective optimization problems in engineering design. The algorithm uniquely combines genetic operators with the Random Opposition-Based Learning (ROBL) strategy, optimizing both local and global search capabilities. Further enhancement is achieved through the integration of elitist non-dominated sorting (NDS), information feedback mechanism (IFM) and Crowding Distance (CD) selection method, which collectively aim to efficiently identify the Pareto optimal front. The performance of MOLCA is rigorously assessed using a comprehensive set of standard multi-objective test benchmarks, including ZDT, DTLZ and various Constraint (CONSTR, TNK, SRN, BNH, OSY and KITA) and real-world engineering design problems like Brushless DC wheel motor, Safety isolating transformer, Helical spring, Two-bar truss and Welded beam. Its efficacy is benchmarked against prominent algorithms such as the non-dominated sorting grey wolf optimizer (NSGWO), multiobjective multi-verse optimization (MOMVO), non-dominated sorting genetic algorithm (NSGA-II), decomposition-based multiobjective evolutionary algorithm (MOEA/D) and multiobjective marine predator algorithm (MOMPA). Quantitative analysis is conducted using GD, IGD, SP, SD, HV and RT metrics to represent convergence and distribution, while qualitative aspects are presented through graphical representations of the Pareto fronts. The MOLCA source code is available at: https://github.com/kanak02/MOLCA.

NSCSO: a novel multi-objective non-dominated sorting chicken swarm optimization algorithm

Addressing the challenge of efficiently solving multi-objective optimization problems (MOP) and attaining satisfactory optimal solutions has always posed a formidable task. In this paper, based on the chicken swarm optimization algorithm, proposes the non-dominated sorting chicken swarm optimization (NSCSO) algorithm. The proposed approach involves assigning ranks to individuals in the chicken swarm through fast non-dominance sorting and utilizing the crowding distance strategy to sort particles within the same rank. The MOP is tackled based on these two strategies, with the integration of an elite opposition-based learning strategy to facilitate the exploration of optimal solution directions by individual roosters. NSCSO and 6 other excellent algorithms were tested in 15 different benchmark functions for experiments. By comprehensive comparison of the test function results and Friedman test results, the results obtained by using the NSCSO algorithm to solve the MOP problem have better performance. Compares the NSCSO algorithm with other multi-objective optimization algorithms in six different engineering design problems. The results show that NSCSO not only performs well in multi-objective function tests, but also obtains realistic solutions in multi-objective engineering example problems.

Optimal scheduling of integrated energy system using decoupled distributed CSO with opposition-based learning and neighborhood re-dispatch strategy

Integrated energy optimization scheduling (IEOS) is a complex problem aiming to minimize the total cost while the requirements of load balance is met. Due to the non-convex, non-differentiable and high-dimensional characteristics, there are many difficulties in solving the problem. Based on a regional integrated energy system (RIES), a decoupled distributed crisscross optimization with opposition-based learning and neighborhood re-dispatch strategy (DDCSO-OBL-NR) is proposed to solve IEOS problem by distributed method with different energy types as the scale. Initially, the CSO with excellent global search ability is firstly used to solve the complicated IEOS problem. Then, based on the distributed structure, distributed parallel computing can be achieved by DDCSO, which contributes to 1) protect the privacy of different energy data, 2) reduce the solving dimensions and 3) relieve the heavy communication burden. The total optimal cost is achieved by minimizing the cost of each portion without centralized controller. Furthermore, the opposition-based learning (OBL) strategy and the neighborhood re-dispatch (NR) strategy are combined into DDCSO aiming to optimize initial population location and enhance local search ability. Eventually, the DDCSO-OBL-NR is realized, and the effectiveness of which in solving the distributed IEOS problems is verified by the experimental results of three cases.

EAO: Enhanced aquila optimizer for solving optimization problem

The Aquila optimization (AO) algorithm has the drawbacks of local optimization and poor optimization accuracy when confronted with complex optimization problems. To remedy these drawbacks, this paper proposes an Enhanced aquila optimization (EAO) algorithm. To avoid elite individual from entering the local optima, the elite opposition-based learning strategy is added. To enhance the ability of balancing global exploration and local exploitation, a dynamic boundary strategy is introduced. To elevate the algorithm’s convergence rapidity and precision, an elite retention mechanism is introduced. The effectiveness of EAO is evaluated using CEC2005 benchmark functions and four benchmark images. The experimental results confirm EAO’s viability and efficacy. The statistical results of Freidman test and the Wilcoxon rank sum test are confirmed EAO’s robustness. The proposed EAO algorithm outperforms previous algorithms and can useful for threshold optimization and pressure vessel design.

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.

Dynamic allocation of opposition-based learning in differential evolution for multi-role individuals

<abstract><p>Opposition-based learning (OBL) is an optimization method widely applied to algorithms. Through analysis, it has been found that different variants of OBL demonstrate varying performance in solving different problems, which makes it crucial for multiple OBL strategies to co-optimize. Therefore, this study proposed a dynamic allocation of OBL in differential evolution for multi-role individuals. Before the population update in DAODE, individuals in the population played multiple roles and were stored in corresponding archives. Subsequently, different roles received respective rewards through a comprehensive ranking mechanism based on OBL, which assigned an OBL strategy to maintain a balance between exploration and exploitation within the population. In addition, a mutation strategy based on multi-role archives was proposed. Individuals for mutation operations were selected from the archives, thereby influencing the population to evolve toward more promising regions. Experimental results were compared between DAODE and state of the art algorithms on the benchmark suite presented at the 2017 IEEE conference on evolutionary computation (CEC2017). Furthermore, statistical tests were conducted to examine the significance differences between DAODE and the state of the art algorithms. The experimental results indicated that the overall performance of DAODE surpasses all state of the art algorithms on more than half of the test functions. Additionally, the results of statistical tests also demonstrated that DAODE consistently ranked first in comprehensive ranking.</p></abstract>