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

Hazim Nasir Ghafil

doi:10.1016/j.asoc.2020.106392

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

2020-08-01

Hazim Nasir Ghafil *

Kâroly Jármai

DOI：10.1016/j.asoc.2020.106392

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Abstract

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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.

Keywords:

Dynamic differential annealed optimization

Optimization algorithms

Computational intelligence

Path planning

Engineering design optimization

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