6 research outputs found
Multimodal estimation of distribution algorithms
Taking the advantage of estimation of distribution algorithms (EDAs) in preserving high diversity, this paper proposes a multimodal EDA. Integrated with clustering strategies for crowding and speciation, two versions of this algorithm are developed, which operate at the niche level. Then these two algorithms are equipped with three distinctive techniques: 1) a dynamic cluster sizing strategy; 2) an alternative utilization of Gaussian and Cauchy distributions to generate offspring; and 3) an adaptive local search. The dynamic cluster sizing affords a potential balance between exploration and exploitation and reduces the sensitivity to the cluster size in the niching methods. Taking advantages of Gaussian and Cauchy distributions, we generate the offspring at the niche level through alternatively using these two distributions. Such utilization can also potentially offer a balance between exploration and exploitation. Further, solution accuracy is enhanced through a new local search scheme probabilistically conducted around seeds of niches with probabilities determined self-adaptively according to fitness values of these seeds. Extensive experiments conducted on 20 benchmark multimodal problems confirm that both algorithms can achieve competitive performance compared with several state-of-the-art multimodal algorithms, which is supported by nonparametric tests. Especially, the proposed algorithms are very promising for complex problems with many local optima
Niching particle swarm optimization based euclidean distance and hierarchical clustering for multimodal optimization
Abstract : Multimodal optimization is still one of the most challenging tasks in the evolutionary computation field, when multiple global and local optima need to be effectively and efficiently located. In this paper, a niching Particle Swarm Optimization (PSO) based Euclidean Distance and Hierarchical Clustering (EDHC) for multimodal optimization is proposed. This technique first uses the Euclidean distance based PSO algorithm to perform preliminarily search. In this phase, the particles are rapidly clustered around peaks. Secondly, hierarchical clustering is applied to identify and concentrate the particles distributed around each peak to finely search as a whole. Finally, a small world network topology is adopted in each niche to improve the exploitation ability of the algorithm. At the end of this paper, the proposed EDHC-PSO algorithm is applied to the Traveling Salesman Problems (TSP) after being discretized. The experiments demonstrate that the proposed method outperforms existing niching techniques on benchmark problems, and is effective for TSP
Adaptive multimodal continuous ant colony optimization
Seeking multiple optima simultaneously, which multimodal optimization aims at, has attracted increasing attention but remains challenging. Taking advantage of ant colony optimization algorithms in preserving high diversity, this paper intends to extend ant colony optimization algorithms to deal with multimodal optimization. First, combined with current niching methods, an adaptive multimodal continuous ant colony optimization algorithm is introduced. In this algorithm, an adaptive parameter adjustment is developed, which takes the difference among niches into consideration. Second, to accelerate convergence, a differential evolution mutation operator is alternatively utilized to build base vectors for ants to construct new solutions. Then, to enhance the exploitation, a local search scheme based on Gaussian distribution is self-adaptively performed around the seeds of niches. Together, the proposed algorithm affords a good balance between exploration and exploitation. Extensive experiments on 20 widely used benchmark multimodal functions are conducted to investigate the influence of each algorithmic component and results are compared with several state-of-the-art multimodal algorithms and winners of competitions on multimodal optimization. These comparisons demonstrate the competitive efficiency and effectiveness of the proposed algorithm, especially in dealing with complex problems with high numbers of local optima
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μ 2 μ₯ μꡬμμ μλκ²μΆκΈ° 6
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3.1 μ¬λ€λ¦¬κΌ΄ μκΈ°μ λ ₯μ μ΄μ©ν μ€κ³ 10
3.2 μ νν μκΈ°μ λ ₯μ μ΄μ©ν μ€κ³ 29
μ 4 μ₯ λΈλ¬μ¬ μλ μꡬμμ μλκ²μΆκΈ° μ΅μ μ€κ³ 41
4.1 κΈ°μ‘΄μ μ΅μ ν κΈ°λ² 42
4.2 μ μλ μ΅μ ν κΈ°λ² 46
4.3 μ μλ μ΅μ ν κΈ°λ²μ μ΄μ©ν μꡬμμ μλκ²μΆκΈ° μ΅μ μ€κ³ 59
μ 5 μ₯ μμ ν μ€κ³, μ μ λ° νκ° 76
5.1 μ¬λ€λ¦¬κΌ΄ μκΈ°μ λ ₯μ μ΄μ©ν μλκ²μΆκΈ° μμ ν 77
5.2 μ νν μκΈ°μ λ ₯μ μ΄μ©ν μλκ²μΆκΈ° μμ ν 86
μ 6 μ₯ λΈλ¬μ¬ μλ μꡬμμ μλκ²μΆκΈ°λ₯Ό μ΄μ©ν μ λν λ κ° κ΅¬λμ₯μΉ κ³΅νμ± μ§λ μ΅μ μ μ΄ 95
6.1 μ λν λ κ° κ³΅νμ± μ§λ νμ 95
6.2 λΈλ¬μ¬ μλ μꡬμμ μλκ²μΆκΈ°λ₯Ό μ΄μ©ν 곡νμ± μ§λ μ΅μ μ μ΄ 96
μ 7 μ₯ κ²°λ‘ λ° ν₯ν μ°κ΅¬κ³ν 114
7.1 κ²°λ‘ 114
7.2 ν₯ν μ°κ΅¬κ³ν 115
μ°Έκ³ λ¬Έν 117
Abstract 129Docto