本研究所提出以基因演算法為基礎的架構，係依混沌映射及類聚分群的模式，並承續基因演算法的特性所構成。混沌映射的遍歷、不重複的特性，可以有效的提昇尋獲最佳解的效率；而援引類聚分群演算法的群聚特性，可以增進系統的收斂效果。在結合上述混沌、類聚分群、基因演算法等各演算邏輯的新演算法中，將展現出優異的演化效果。

研究中所提出將粒子群最佳化演算法，結合混沌映射和動態導引機制後，即擁有遍歷、不重複和動態、難以預測的屬性；進一步將混沌映射轉化為搜尋機制後，並配合對全域最佳解動態導引的設計，可以促使系統同時具有快速搜尋，和從區域最佳解脫離的潛在優勢，因而有助於演算法尋獲最佳解的效率。

證諸實際驗證的結果，並與其他傳統的啟發式演算法比較，顯示本研究根據基因演算法、及粒子群最佳化演算法，所提出的「混沌類聚分群基因演算法」及「混沌動態導引粒子群最佳化演算法」，可以顯著的提高尋獲最佳解的效能；而在求解最佳化問題時，本研究所提出的演算法更具有相對的優勢。

The proposed approach brings up a manner of cognition that inherits the paradigm in Genetic Algorithms (GA) to implement a chaotic mapping and enhanced by K-means clustering approach. Chaotic mapping, enjoyed ergodicity, irregularity and the stochastic properties in heuristic optimization approach, contributes to global search evidently. In the meantime, K-means clustering approach with grouping property in the proposed algorithm could more easily lead to convergence.

Dynamic guiding approach coupled with Particle Swarm Optimization (PSO) often implied fluctuation movement during evolution. Chaos properties employed in PSO were to search for optimal solution. The proposed algorithm incorporated dynamic guiding approach and chaos attributions into PSO would inevitably bring to a desirable optimal solution.

As proven in multi-dimensional design space and compared with other conventional heuristic based algorithms, the proposed approaches could improve the search performance significantly, and show the superiority in solving optimization problems over other algorithms reported in this study.

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