In practical engineering,multi-objectiveoptimization often encounters situations where multiple Pareto sets(PS)in the decision space correspond to the same Pareto front(PF)in the objective space,known as multi-Modal ...
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In practical engineering,multi-objectiveoptimization often encounters situations where multiple Pareto sets(PS)in the decision space correspond to the same Pareto front(PF)in the objective space,known as multi-Modal multi-objectiveoptimizationproblems(MMOP).Locating multiple equivalent global PSs poses a significant challenge in real-world applications,especially considering the existence of local *** identifying and locating both global and local PSs is a major *** tackle this issue,we introduce an immune-inspired reproduction strategy designed to produce more offspring in less crowded,promising regions and regulate the number of offspring in areas that have been thoroughly *** approach achieves a balanced trade-off between exploration and ***,we present an interval allocation strategy that adaptively assigns fitness levels to each *** strategy ensures a broader survival margin for solutions in their initial stages and progressively amplifies the differences in individual fitness values as the population matures,thus fostering better population ***,we incorporate a multi-population mechanism that precisely manages each subpopulation through the interval allocation strategy,ensuring the preservation of both global and local *** results on 21 test problems,encompassing both global and local PSs,are compared with eight state-of-the-art multimodalmulti-objectiveoptimization *** results demonstrate the effectiveness of our proposed algorithm in simultaneously identifying global Pareto sets and locally high-quality PSs.
multimodal multi-objective optimization problems (MMOPs) refer to the problems that have multiple Pareto-optimal solution sets in decision space corresponding to the same or similar Pareto-optimal front in objective s...
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multimodal multi-objective optimization problems (MMOPs) refer to the problems that have multiple Pareto-optimal solution sets in decision space corresponding to the same or similar Pareto-optimal front in objective space. These problems require the optimization algorithm to locate multiple Pareto Sets (PSs). This paper proposes a differential evolution algorithm based on the clustering technique and an elite selection mechanism to solve MMOPs. In this algorithm, a Clustering-based Special Crowding Distance (CSCD) method is designed to calculate the comprehensive crowding degree in decision and objective spaces. Subsequently, a distance-based elite selection mechanism (DBESM) is introduced to determine the learning exemplars of various individuals. New individuals are generated around the exemplars to obtain a well-distributed population in both decision and objective spaces. To test the performance of the proposed algorithm, extensive experiments on the suit of CEC'2019 benchmark functions have been conducted. The results indicate that the proposed method has superior performance compared with other commonly used algorithms.
In the past decades, various effective and efficient multi-objective evolutionary algorithms (MOEAs) have been proposed for solving multi-objectiveoptimizationproblems. However, existing MOEAs cannot satisfactorily ...
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In the past decades, various effective and efficient multi-objective evolutionary algorithms (MOEAs) have been proposed for solving multi-objectiveoptimizationproblems. However, existing MOEAs cannot satisfactorily address multimodal multi-objective optimization problems that demand to find multiple groups of optimal solutions simultaneously. In this paper, we propose an evolution strategy to solve multimodal multi-objective optimization problems, named MMO-MOES. This paper focus on searching for well-converged and well-distributed solutions in the decision space. Firstly, a novel niching strategy in the decision space, which imitates the repulsive force among isotropic magnetic particles, is adopted to drive the individuals to preserve uniform distances from each other and spread to the whole Pareto set automatically. This strategy is effective in finding multiple groups of optimal solutions simultaneously. Secondly, MMO-MOES requires only a very small population size to obtain a well-distributed and well-converged set of Pareto optimal solutions in the decision space. The greater the population size, the clearer contour of the approximate Pareto sets and Pareto front will be. Finally, the MMO-MOES is compared against some chosen leading-edge MMOEAs. The experimental results demonstrate that MMO-MOES provides exceptional performance in searching for the complete Pareto subsets and Pareto front on Omni-test problem, Symmetrical Parts (SYM-PART) problems, and CEC 2019 multimodal multi-objective optimization problems (MMOPs) test suite. (C) 2020 Elsevier B.V. All rights reserved.
In recent years, numerous efficient and effective multimodalmulti-objective evolutionary algorithms (MMOEAs) have been developed to address multimodal multi-objective optimization problems (MMOPs) involving multiple ...
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ISBN:
(纸本)9781665441957
In recent years, numerous efficient and effective multimodalmulti-objective evolutionary algorithms (MMOEAs) have been developed to address multimodal multi-objective optimization problems (MMOPs) involving multiple equivalent sets of Pareto optimal solutions to be found simultaneously. However, the Pareto optimal solutions may have various contracting or expending shapes, and have random locations in the decision space. In addition, uniform decision distribution does not imply good objective distribution. Therefore, many existing MMOEAs are very difficult to guide the individuals converged to every Pareto subregion with good distribution in both the decision space and the objective space. In this paper, we present a multi-population evolutionary algorithm to search for the equivalent global Pareto optimal solutions. The original population should be divided into two groups of subpopulations with equal size. The first subpopulation is designed to search for the optimal solutions in objective space. At the same time. the second subpopulation focus to obtain high-quality optimal solutions in the decision space. The multi-population strategy is helpful to improve the decision and objective distributions simultaneously, and address the MMOPs effectively. The proposed algorithm is compared against five state-of-the-art MMOEAs. The experimental results indicate the proposed algorithm provides better performance than competing MMOEAs on IEEE CEC 2019 MMOPs test suite.
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