Multimodal Optimization Evolutionary Algorithm for RNA Secondary Structure Prediction

Hu, Yunfei; Zhang, Kai

doi:10.1145/3469678.3469714

Cited by 2 publications

(1 citation statement)

References 14 publications

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“…Multimodal optimization problems (MOPs) are commonly encountered in scientifc research and engineering design. Some examples of MOPs include RNA secondary structure prediction [1], motion planning of robot arms [2], trajectory planning of railway vehicles [3], image segmentation [4], and root fnding of nonlinear equation systems [5]. Terefore, efcient algorithms for solving MOPs are of great importance for progressing the development of new technologies.…”

Section: Introductionmentioning

confidence: 99%

Differential Evolution with a Level‐Based Learning Strategy for Multimodal Optimization

Zhang,

Wei,

Zhao

et al. 2023

International Journal of Intelligent Systems

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Multimodal optimization aims at efficiently finding multiple optimal solutions of a problem. Owing to the population-based search mechanism, evolutionary algorithms (EAs) are becoming increasingly popular in solving multimodal optimization problems (MOPs). Most existing work focuses on designing and incorporating niching techniques into EAs so that multiple subpopulations can be formed and assigned to locate different optima. To further enhance the exploration and exploitation abilities of existing EAs, this paper developed a multimodal level-based learning strategy. The basic idea is that individuals should be treated differently according to their positions in the subpopulation. In the evolutionary process, a subpopulation is formed for each candidate solution by grouping its neighboring solutions. Then, individuals in the subpopulation are sorted according to their fitness. Subsequently, the multimodal level-based learning strategy applies different mutation operators to different individuals according to their rankings. Experiments are conducted on a set of benchmark problems to verify the efficacy of the multimodal level-based learning strategy. The results show that the proposed learning strategy can significantly enhance the performance of the existing algorithm. In addition, the algorithm integrated with the proposed strategy is applied to the task of finding multiple roots of nonlinear equation systems (NESs). The results indicate that with the support of the proposed learning strategy, the integrated algorithm compares favorably with state-of-the-art root finding algorithms.

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Section: Introductionmentioning

confidence: 99%

Differential Evolution with a Level‐Based Learning Strategy for Multimodal Optimization

Zhang,

Wei,

Zhao

et al. 2023

International Journal of Intelligent Systems

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Brain Storm Optimization Algorithm with an Adaptive Parameter Control Strategy for Finding Multiple Optimal Solutions

Zhang,

Wei,

Xie

et al. 2023

Int J Comput Intell Syst

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Real-world optimization problems often have multiple optimal solutions and simultaneously finding these optimal solutions is beneficial yet challenging. Brain storm optimization (BSO) is a relatively new paradigm of swarm intelligence algorithm that has been shown to be effective in solving global optimization problems, but it has not been fully exploited for multimodal optimization problems. A simple control strategy for the step size parameter in BSO cannot meet the need of optima finding task in multimodal landscapes and can possibly be refined and optimized. In this paper, we propose an adaptive BSO (ABSO) algorithm that adaptively adjusts the step size parameter according to the quality of newly created solutions. Extensive experiments are conducted on a set of multimodal optimization problems to evaluate the performance of ABSO and the experimental results show that ABSO outperforms existing BSO algorithms and some recently developed algorithms. BSO has great potential in multimodal optimization and is expected to be useful for solving real-world optimization problems that have multiple optimal solutions.

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Multimodal Optimization Evolutionary Algorithm for RNA Secondary Structure Prediction

Cited by 2 publications

References 14 publications

Differential Evolution with a Level‐Based Learning Strategy for Multimodal Optimization

Differential Evolution with a Level‐Based Learning Strategy for Multimodal Optimization

Brain Storm Optimization Algorithm with an Adaptive Parameter Control Strategy for Finding Multiple Optimal Solutions

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