6 research outputs found

    Multimodal estimation of distribution algorithms

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    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

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    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

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    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

    κ³ μ‹ λ’°μ„± μœ λ„λ¬΄κΈ°μš© λΈŒλŸ¬μ‰¬ μ—†λŠ” μ˜κ΅¬μžμ„ μ†λ„κ²€μΆœκΈ° 졜적 섀계

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    ν•™μœ„λ…Όλ¬Έ (박사)-- μ„œμšΈλŒ€ν•™κ΅ λŒ€ν•™μ› : 전기·컴퓨터곡학뢀, 2016. 8. μ •ν˜„κ΅.μœ λ„λ¬΄κΈ° 및 무인 항곡기와 같은 κ΅­λ°© 및 항곡 λΆ„μ•Ό ꡬ동μž₯μΉ˜λ‘œλŠ” 전동기λ₯Ό μ΄μš©ν•œ 전기식 ꡬ동μž₯μΉ˜κ°€ 널리 μ‚¬μš©λ˜κ³  있으며, μ΄λŸ¬ν•œ 전기식 ꡬ동μž₯치λ₯Ό μ œμ–΄ν•˜κΈ° μœ„ν•΄μ„œλŠ” νšŒμ „μ†λ„ μ„Όμ„œκ°€ ν•„μˆ˜μ μœΌλ‘œ μš”κ΅¬λœλ‹€. ν˜„μž¬ κ΅­λ‚΄μ™Έμ—μ„œ 개발 μ™„λ£Œ λ˜μ—ˆκ±°λ‚˜, 개발 진행 쀑인 μœ λ„λ¬΄κΈ°μš© 전기식 ꡬ동μž₯μΉ˜μ— μ‚¬μš©λ˜λŠ” νšŒμ „μ†λ„ μ„Όμ„œλ‘œλŠ” 직λ₯˜ μ˜κ΅¬μžμ„ μ†λ„κ²€μΆœκΈ°(DC Tachogenerator)κ°€ κ°€μž₯ 많이 μ‚¬μš©λ˜κ³  μžˆλ‹€. 직λ₯˜ μ˜κ΅¬μžμ„ μ†λ„κ²€μΆœκΈ°λŠ” 직λ₯˜ λ°œμ „κΈ° 원리λ₯Ό μ΄μš©ν•˜λŠ” κ°„λ‹¨ν•œ ꡬ쑰λ₯Ό κ°€μ§€λ―€λ‘œ μ†Œν˜•μœΌλ‘œ κ΅¬ν˜„μ΄ κ°€λŠ₯ν•˜λ©°, 여기전압이 λΆˆν•„μš”ν•˜κ³ , 속도에 λΉ„λ‘€ν•˜λŠ” μ „μ•• 좜λ ₯을 λΉ λ₯΄κ³  μ†μ‰½κ²Œ 얻을 수 μžˆλŠ” μž₯점이 μžˆλ‹€. ν•˜μ§€λ§Œ 직λ₯˜ μ˜κ΅¬μžμ„ μ†λ„κ²€μΆœκΈ°λŠ” κΈ°κ³„μ μœΌλ‘œ λΈŒλŸ¬μ‰¬λ₯Ό ν†΅ν•˜μ—¬ μ ‘μ΄‰ν•˜λŠ” ꡬ쑰λ₯Ό 가지고 있기 λ•Œλ¬Έμ—, 진동 및 좩격 등이 μ§€μ†μ μœΌλ‘œ μΈκ°€λ˜λŠ” κ°€ν˜Ήν•œ κ΅°μ‚¬ν™˜κ²½ 쑰건에 λŒ€ν•œ λ‚΄ν™˜κ²½μ„± μΈ‘λ©΄μ—μ„œ λΆˆλ¦¬ν•˜κ³ , 고속 νšŒμ „ν•˜λŠ” 전동기에 μ‚¬μš©ν•˜κΈ° νž˜λ“€λ©°, λΈŒλŸ¬μ‰¬μ˜ 기계적 마λͺ¨μ— μ˜ν•œ μ‚¬μš©μ‹œκ°„ μ œν•œ 및 μ „μžνŒŒ 간섭에 μ˜ν•œ μ‹ ν˜Έ 작음 λ°œμƒ λ“±μ˜ λ¬Έμ œμ μ„ κ°€μ§€κ²Œ λœλ‹€. λ”°λΌμ„œ, λ³Έ λ…Όλ¬Έμ—μ„œλŠ” 직λ₯˜ μ˜κ΅¬μžμ„ μ†λ„κ²€μΆœκΈ°μ˜ μš°μˆ˜ν•œ μž₯점듀을 μœ μ§€ν•˜λ©΄μ„œλ„ λΈŒλŸ¬μ‰¬μ˜ μ‚¬μš©μœΌλ‘œ μΈν•œ 단점듀을 κ·Ήλ³΅ν•¨μœΌλ‘œμ¨, μœ λ„λ¬΄κΈ°μ—μ„œ μš”κ΅¬ν•˜λŠ” 높은 μ•ˆμ •μ„±κ³Ό 신뒰성을 κ°€μ§ˆ 수 μžˆλŠ” λΈŒλŸ¬μ‰¬ μ—†λŠ” μ˜κ΅¬μžμ„ μ†λ„κ²€μΆœκΈ°(Brushless Tachogenerator)λ₯Ό μ œμ•ˆν•˜κ³ , 이의 졜적 μ„€κ³„μ•ˆμ„ μ œμ‹œν•œλ‹€. μ œμ•ˆλœ λΈŒλŸ¬μ‰¬ μ—†λŠ” μ˜κ΅¬μžμ„ μ†λ„κ²€μΆœκΈ°λŠ” ꡐλ₯˜ λ°œμ „κΈ° 원리λ₯Ό μ΄μš©ν•˜κΈ° λ•Œλ¬Έμ— κ΅­λ‚΄μ˜ 전동기 및 λ°œμ „κΈ° μ œμ‘°μ‹œμ„€ κΈ°λ°˜μ„ ν™œμš©ν•˜μ—¬ μ œμž‘μ΄ κ°€λŠ₯ν•˜λ‹€. λ”°λΌμ„œ, κ΅°μ‚¬μš© λͺ©μ  μ‚¬μš©μ— λ”°λ₯Έ ν•΄μ™Έ λ„μž…ν’ˆμ˜ 수좜 κ·œμ œμ™€ 관계없이 κ΅­λ‚΄μ—μ„œ λ…μž 개발이 κ°€λŠ₯ν•œ μž₯점을 가진닀. λ³Έ λ…Όλ¬Έμ—μ„œλŠ” λΈŒλŸ¬μ‰¬ μ—†λŠ” μ˜κ΅¬μžμ„ μ†λ„κ²€μΆœκΈ°μ˜ νšŒμ „μ†λ„ 및 νšŒμ „λ°©ν–₯ κ΅¬ν˜„ 기법을 μƒˆλ‘­κ²Œ μ œμ•ˆν•˜κ³ , λΈŒλŸ¬μ‰¬ μ—†λŠ” μ˜κ΅¬μžμ„ μ†λ„κ²€μΆœκΈ° 운용 쀑에 단선에 μ˜ν•΄ 3개의 상역기전압 쀑 1κ°œκ°€ κ²€μΆœμ΄ λΆˆκ°€λŠ₯ν•˜λ”λΌλ„ μ„Όμ„œ 자체적으둜 이λ₯Ό κ·Ήλ³΅ν•˜λŠ” λ‚΄κ³ μž₯μ„± 확보 방법에 λŒ€ν•˜μ—¬ μ œμ•ˆν•¨μœΌλ‘œμ¨ μ†λ„κ²€μΆœκΈ°μ˜ μ‹ λ’°μ„± 및 μ•ˆμ •μ„±μ„ ν–₯μƒμ‹œμΌ°λ‹€. λ˜ν•œ, λ³Έ λ…Όλ¬Έμ—μ„œλŠ” λ³΅μž‘ν•œ λͺ©μ ν•¨μˆ˜λ₯Ό 가지며 였랜 κ³„μ‚°μ‹œκ°„μ΄ μ†Œμš”λ˜λŠ” λΈŒλŸ¬μ‰¬ μ—†λŠ” μ˜κ΅¬μžμ„ μ†λ„κ²€μΆœκΈ°μ™€ 같은 μ „κΈ°κΈ°κΈ° μ΅œμ μ„€κ³„ 문제λ₯Ό 효과적으둜 ν•΄κ²°ν•  수 μžˆλŠ” μƒˆλ‘œμš΄ λŒ€λ¦¬λͺ¨λΈ 기반 λ©€ν‹°λͺ¨λ‹¬ μ΅œμ ν™” μ•Œκ³ λ¦¬μ¦˜μ„ μ œμ•ˆν•˜κ³ , 이λ₯Ό λ°”νƒ•μœΌλ‘œ λΈŒλŸ¬μ‰¬ μ—†λŠ” μ˜κ΅¬μžμ„ μ†λ„κ²€μΆœκΈ°μ— λŒ€ν•œ 졜적 섀계λ₯Ό μˆ˜ν–‰ν•˜μ˜€μœΌλ©°, μ‹€μ œ μ‹œμ œν’ˆμ„ μ œμž‘ν•˜κ³  이에 λŒ€ν•˜μ—¬ λ‹€μ–‘ν•œ μ‹œν—˜μ„ μˆ˜ν–‰ν•¨μœΌλ‘œμ¨ μ œμ•ˆλœ 섀계기법 및 μ‹œμ œν’ˆμ˜ μ„±λŠ₯을 μž…μ¦ν•˜μ˜€λ‹€. λ§ˆμ§€λ§‰μœΌλ‘œ μ œμ•ˆλœ λΈŒλŸ¬μ‰¬ μ—†λŠ” μ˜κ΅¬μžμ„ μ†λ„κ²€μΆœκΈ°λ₯Ό μ΄μš©ν•˜μ—¬ μœ λ„νƒ„ λ‚ κ°œ ꡬ동μž₯μΉ˜μ— λ°œμƒν•œ 곡기역학적 곡탄성 진동을 효과적으둜 μ–΅μ œν•  수 μžˆλŠ” μƒˆλ‘œμš΄ μ œμ–΄ 기법을 μ œμ‹œν•˜κ³ , 이에 λŒ€ν•œ κ²€μ¦μ‹œν—˜μ„ μˆ˜ν–‰ν•¨μœΌλ‘œμ¨, μ œμ•ˆλœ κΈ°λ²•μ˜ μš°μˆ˜ν•œ μ„±λŠ₯을 ν™•μΈν•˜μ˜€λ‹€.제 1 μž₯ μ„œλ‘  1 1.1 연ꡬ배경 및 λͺ©μ  1 1.2 λ…Όλ¬Έ ꡬ성 4 제 2 μž₯ μ˜κ΅¬μžμ„ μ†λ„κ²€μΆœκΈ° 6 제 3 μž₯ λΈŒλŸ¬μ‰¬ μ—†λŠ” μ˜κ΅¬μžμ„ μ†λ„κ²€μΆœκΈ° 섀계 기법 9 3.1 사닀리꼴 μ—­κΈ°μ „λ ₯을 μ΄μš©ν•œ 섀계 10 3.2 μ •ν˜„νŒŒ μ—­κΈ°μ „λ ₯을 μ΄μš©ν•œ 섀계 29 제 4 μž₯ λΈŒλŸ¬μ‰¬ μ—†λŠ” μ˜κ΅¬μžμ„ μ†λ„κ²€μΆœκΈ° 졜적 섀계 41 4.1 기쑴의 μ΅œμ ν™” 기법 42 4.2 μ œμ•ˆλœ μ΅œμ ν™” 기법 46 4.3 μ œμ•ˆλœ μ΅œμ ν™” 기법을 μ΄μš©ν•œ μ˜κ΅¬μžμ„ μ†λ„κ²€μΆœκΈ° 졜적 섀계 59 제 5 μž₯ μ‹œμ œν’ˆ 섀계, μ œμž‘ 및 평가 76 5.1 사닀리꼴 μ—­κΈ°μ „λ ₯을 μ΄μš©ν•œ μ†λ„κ²€μΆœκΈ° μ‹œμ œν’ˆ 77 5.2 μ •ν˜„νŒŒ μ—­κΈ°μ „λ ₯을 μ΄μš©ν•œ μ†λ„κ²€μΆœκΈ° μ‹œμ œν’ˆ 86 제 6 μž₯ λΈŒλŸ¬μ‰¬ μ—†λŠ” μ˜κ΅¬μžμ„ μ†λ„κ²€μΆœκΈ°λ₯Ό μ΄μš©ν•œ μœ λ„νƒ„ λ‚ κ°œ ꡬ동μž₯치 곡탄성 진동 μ–΅μ œ μ œμ–΄ 95 6.1 μœ λ„νƒ„ λ‚ κ°œ 곡탄성 진동 ν˜„μƒ 95 6.2 λΈŒλŸ¬μ‰¬ μ—†λŠ” μ˜κ΅¬μžμ„ μ†λ„κ²€μΆœκΈ°λ₯Ό μ΄μš©ν•œ 곡탄성 진동 μ–΅μ œ μ œμ–΄ 96 제 7 μž₯ κ²°λ‘  및 ν–₯ν›„ μ—°κ΅¬κ³„νš 114 7.1 κ²°λ‘  114 7.2 ν–₯ν›„ μ—°κ΅¬κ³„νš 115 μ°Έκ³  λ¬Έν—Œ 117 Abstract 129Docto

    A Cluster-Based Differential Evolution With Self-Adaptive Strategy for Multimodal Optimization

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