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Dynamic differential annealed optimization: New metaheuristic optimization algorithm for engineering applications

动态微分退火的优化: 为设计应用的新 metaheuristic 优化算法

作     者:Ghafil, Hazim Nasir Jarmai, Karoly 

作者机构:Univ Miskolc H-3515 Miskolc Egyetemvaros Hungary Univ Kufa Fac Engn Najaf Iraq 

出 版 物:《APPLIED SOFT COMPUTING》 (应用软计算)

年 卷 期:2020年第93卷

页      面:106392-106392页

核心收录:

学科分类:08[工学] 0812[工学-计算机科学与技术(可授工学、理学学位)] 

主  题:Dynamic differential annealed optimization Optimization algorithms Computational intelligence Path planning Engineering design optimization 

摘      要:This work proposes a novel optimization algorithm which can be used to solve a wide range of mathematical optimization problems where the global minimum or maximum is required. The new algorithm is based on random search and classical simulated annealing algorithm (it mimics the modern process of producing high-quality steel) and is designated dynamic differential annealed optimization (DDAO). The proposed algorithm was benchmarked for 51 test functions. The dynamic differential annealed optimization algorithm has been compared to a large number of highly cited optimization algorithms. Over numerical tests, DDAO has outperformed some of these algorithms in many cases and shown high performance. Constrained path planning and spring design problem were selected as a practical engineering optimization problem. DDAO converged to the global minimum of problems efficiently, and for spring design problem DDAO has found the best feasible solution than what is found by many algorithms. (C) 2020 The Author(s). Published by Elsevier B.V.

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