multi-modal optimization problems (MMOPs) are pivotal in industrial production and scientific research. Unlike standard optimizationproblems, MMOPs aim to identify multiple global solutions, offering users a variety ...
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ISBN:
(纸本)9789819771806;9789819771813
multi-modal optimization problems (MMOPs) are pivotal in industrial production and scientific research. Unlike standard optimizationproblems, MMOPs aim to identify multiple global solutions, offering users a variety of optimal choices. However, traditional optimization algorithms often encounter difficulties when tackling MMOPs. To overcome this challenge, we propose a pretreatment mechanism based on individual distribution information, which is devised to enhance optimization algorithms' performance while preserving its convergence capability. We comprehensively evaluate our method's efficacy using 20 MMOPs from the CEC2013 benchmark suite, comparing it against the widely recognized "crowding method," a prevalent niching strategy. Our findings unequivocally showcase the effectiveness of the proposed mechanism in expediting MMOP optimization. Furthermore, we delve into an analysis elucidating the underlying reasons behind our proposal's effectiveness for MMOPs and discuss potential topics for future enhancements.
This paper presents a niching-based evolutionary algorithm for optimizing multi-modaloptimization function. Provided that the potential optima are characterized by a relatively smaller objective value than their neig...
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This paper presents a niching-based evolutionary algorithm for optimizing multi-modaloptimization function. Provided that the potential optima are characterized by a relatively smaller objective value than their neighbors and by a relatively large distance from points with smaller objective values, we identify potential optima from individuals. Using them as seeds, a population is decomposed into a number of subpopulations without introducing new parameters. Moreover, we present an adaptive allocating strategy of assigning different computational resources to different subpopulations upon the fact that discovering different optima may have different computational difficulty. The proposed method is compared with three state-of-the-art multi-modaloptimization approaches on a benchmark function set. The extensive experimental results demonstrate its efficacy.
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