DE algorithm is among the most successful algorithm for numerical optimization. However, like other metaheuristics, DE suffers from several weaknesses like weak exploration and local minimum stagnation problems. Besides, most DE variants including the most efficient ones like LSHADE variants, suffer in presence of hard composition functions having global optima hard to reach. On the other hand, Tangent Search Algorithm (TSA) has shown an effective ability to deal with hard optimization functions thanks to the tangent flight operator. This one offers an effective way to escape from local optima of hard test functions while preserving good exploration ability. In this scope, a hybrid TSA and LSHADE algorithm called TSALSHADE is proposed. The main advantage of the new proposed algorithm is its ability to deal with hard composite functions. The experimental study on the latest CEC 2022 benchmark functions has shown that TSALSHADE is capable to supply very promising and competitive results on most benchmark functions thanks to a better balance between exploration and exploitation of the search.
인용 양식
abdesslem layeb (2024). TSALSHADE: Improved LSHADE Algorithm with Tangent Search (https://www.mathworks.com/matlabcentral/fileexchange/123400-tsalshade-improved-lshade-algorithm-with-tangent-search), MATLAB Central File Exchange. 검색됨 .
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