本研究提出了一種新的灰狼優化混合算法（GWO），結合強健型學習機制，解決了大規模經濟負荷調度（ELD）問題。強健型學習灰狼優化（RLGWO）算法模擬了灰狼在自然界中的狩獵行為和社會等級，並通過基於調整搜索方向和相反學習強健容忍性來加強。該技術可以有效地防止代理搜索陷入局部最優解，也可以產生可能的候選解以獲得可行的解決方案。發電機的若干限制，例如發電限制，區域需求，閥門點效應和傳輸損耗都被考慮用於實際操作。已經使用五個測試系統來評估所提出的算法在解決ELD問題中的有效性和強健性。模擬結果與以往的文獻相比，明顯地顯示了提出的方法在燃料成本和計算效率方面找到更好解的優越性和可行性

This research proposed a new hybrid algorithm of grey wolf optimization (GWO) integrated with
robust learning mechanism to solve the large scale economic load dispatch (ELD) problem. The
robust learning grey wolf optimization (RLGWO) algorithm imitate the hunting behavior and
social hierarchy of grey wolves in nature and reinforced by robust tolerant based adjust searching
direction and opposite based learning. This technique could effectively prevent search agents
trapping into local optimum but also generate potential candidate to obtain feasible solutions.
Several constraints of power generators such as generation limits, local demand, and valve point
loading effect and transmission losses are all considered for practical operations. Five test systems
have been used to evaluate the effectiveness and robustness of the proposed algorithm in solving
ELD problem. The simulation results distinctly reveal the superiority and feasibility of RLGWO
to find the better solution in term of fuel cost and computational efficiency when comparing with
previous literatures.

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