應用協同合作與自適應途徑增進粒子群最佳化演算法

A Collaborative and Adaptive Approach to Particle Swarm Optimization

陳詳翰(Xiang-Han Chen)

指導教授 ： 李維平

中原大學/商學院/資訊管理學系/碩士(2007年)

https://doi.org/10.6840/cycu200700542

摘要

最佳化是一個被廣泛應用的科學技術，本論文基於粒子群最佳化演算法(Particle Swarm Optimization)提出一個稱為－協作與自適應式粒子群演算法(Collaborative and Adaptive Particle Swarm Optimization)，該機制包含了新的粒子群溝通與學習策略，透過分析菁英粒子群(elitist particles)位置的離散資訊進而影響全體粒子的速度向量。針對求解效能同時兼顧各粒子的多樣性，本研究利用動態機制以隨機的方式選擇使用自適應壓縮因子與三元吸引操作以建構一個新的協同搜尋溝通模式。此策略賦予並保證群種運動之差異同時達到快速收斂與精準搜尋。在實驗方面，本研究選擇了包括多峰標竿測試函數與旅行銷售員(traveling salesman problem)測試集進行比較與驗證。實驗結果顯示，相較於其他PSO演算法，本研究提出之CAPSO演算法在解多峰函數與組合最佳化問題上均有優異的效能表現。

關鍵字

粒子群演算法 ； 演化式計算 ； 壓縮因子 ； 最佳化 ； 自適應方法

並列摘要

This paper presents a modified of particle swarm optimizations (PSOs), the collaborative and adaptive particle swarm optimization (CAPSO), which uses a novel communication and learning strategy whereby elitist particles’ positional dispersive information is used to influence all particles’ velocity. In order to improve the performance of PSO and maintain particle’s diversity based on randomization, adaptive constriction factors and the triad-attractive operation were brought forward. This strategy enables the diversity of the swarm to be preserved to faster convergence and accuracy. Experiments were conducted on multimodal test functions and traveling salesman problem (TSP). The results demonstrate good performance of the CAPSO in solving multimodal problems and combinatorial optimization problem when compared with other PSOs.

並列關鍵字

constriction factor ； Particle swarm optimization ； evolutionary computation ； adaptive method ； optimization

參考文獻

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