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系統識別號 U0026-2412201519125000
論文名稱(中文) 以植基於位置及適應值偏離之調控器增強粒子群優化法
論文名稱(英文) Enhancing Particle Swarm Optimization Using Regulators Based on Location and Fitness Deviation
校院名稱 成功大學
系所名稱(中) 資訊管理研究所
系所名稱(英) Institute of Information Management
學年度 104
學期 1
出版年 104
研究生(中文) 楊哲綜
研究生(英文) Che-Tsung Yang
學號 R78991026
學位類別 博士
語文別 英文
論文頁數 60頁
口試委員 指導教授-王惠嘉
口試委員-高宏宇
口試委員-李健興
口試委員-李偉柏
口試委員-李昇暾
中文關鍵字 加速係數  自適應  慣性權重  粒子群優化 
英文關鍵字 acceleration coefficients  adaptive  inertia weight  particle swarm optimization 
學科別分類
中文摘要 就粒子群優化法(PSO)而言,理想之參數控制應當考慮如何衡量整體粒子群及個別粒子之演化現況,進行偵測並提供回饋做為有效搜尋及演化之依據。有鑒於此,本研究的主要目的在於提出新的衡量指標,以偵測PSO的演化狀態,引入擾動式之慣性權重及加速度係數,以期能有效調控PSO探索搜尋(exploration)及探勘搜尋(exploitation) 之間的變動,藉以增強粒子群優化法之自適應性。
本研究提出兩個新的PSO策略,分別成為稱為PSO-LGR和PSO-FWAC。PSO-LGR作法是:偵測個別粒子與群體目前最佳解間之「位置」的偏離,集成導出慣性權重的調控係數,據以產生適性的演化策略,達成有效的演化。PSO-FWAC,則是根據個別粒子與群體目前最佳解間之「適應值」偏離,集成導出加速係數的調控權重,據以產生適性的演化策略,達成有效的演化。
經實驗驗證以PSO-LGR、PSO-FWAC求最佳解在準確率、成功率及收斂速度上均獲得顯著的改善。本研究證實PSO-LGR、PSO-FWAC能有效度量PSO的演化狀態,提供回饋,有效地控制探索與探勘搜尋之間的轉換,達到增強PSO的目的。本研究主要的貢獻在於:能根據粒子群最佳化法演化的現況,引入有效的擾動,以增強傳統粒子群最佳化法。
英文摘要 In spite of the varying position and fitness of each distinct particle, most of the PSO algorithms treat the given swarm of particles simply. This study aims to find good controls for facilitating exploration and exploitation movements to enhance the traditional particle swarm optimization (PSO) algorithm. In this sense, this study seeks improvements to PSO by introducing adaptive controls on inertia weight and acceleration coefficients according to their corresponding evolutionary states.
Two novel PSO strategies are proposed to facilitate the transitions between searches of exploration and exploitation on the corresponding evolutionary status instead of merely on time (number of iteration). The enhanced particle swarm optimization algorithms, referred as PSO-LGR (location gain regulator) and PSO-FWAC (fitness weighted acceleration coefficients), detect the evolutionary state based on the location and fitness of particles respectively.
Experimental results on widely used benchmark functions show that PSO-LGR and PSO-FWAC outperform the static and time-varying approaches in terms of the precision, success rate, and convergence speed of particle swarm optimization. It is considered as valuable contributions of this study that the proposed regulators are able to enhance traditional PSO by introducing appropriate turbulence depending on corresponding evolutionary states.
論文目次 中文摘要 I
Abstract II
誌謝 III
Chapter 1 Introduction 1
1.1 Motivations and objectives 3
1.2 Two Complemental studies of Research Projects 5
Chapter 2 Literature Review 7
2.1 Reviews on inertia weight strategies 7
2.1.1 Static inertia weight 8
2.1.2 Time-varying inertia weight 9
2.1.3 Dynamic inertia weight 11
2.1.4 Adaptive inertia weight 13
2.2 Reviews on acceleration coefficients 16
2.2.1 Static acceleration coefficients 17
2.2.2 Time-varying acceleration coefficients 18
Chapter 3 Regulated Particle Swarm Optimization 20
3.1 Adaptive inertia weight based on location gain 20
3.2 Fitness-weighted acceleration coefficients 23
Chapter 4 Experiments and Discussions 25
4.1 Project 1– Experimental PSO-LGR 26
4.1.1 Experimental settings 26
4.1.2 Analysis on location gain regulator 31
4.1.3 Statistical analysis 39
4.1.4 Findings and discussions 42
4.2 Project 2 – PSO-FWAC 43
4.2.1 Experimental settings and results 43
4.2.2 Analysis on fitness-weighted regulator 46
4.2.3 Suggested range of cognitive and social constants 51
4.2.4 Findings and Discussions 53
Chapter 5 Conclusions 55
References 58
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