Stochastic Dual Simplex Algorithm: A Novel Heuristic Optimization Algorithm

A new heuristic optimization algorithm is presented to solve the nonlinear optimization problems. The proposed algorithm utilizes a stochastic method to achieve the optimal point based on simplex techniques. A dual simplex is distributed stochastically in the search space to find the best optimal po...

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Vydáno v:IEEE transactions on cybernetics Ročník 51; číslo 5; s. 2725 - 2734
Hlavní autoři: Zandavi, Seid Miad, Chung, Vera Yuk Ying, Anaissi, Ali
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States IEEE 01.05.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Apexes
Benchmark testing
Cybernetics
Genetic algorithms
Heuristic algorithms
Heuristic approach
Optimization
optimization algorithm
Optimization algorithms
Particle swarm optimization
Pattern search
Search algorithms
Search problems
Simplex method
simplex techniques
stochastic dual simplex algorithm (SDSA)
ISSN:2168-2267, 2168-2275, 2168-2275
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Shrnutí:A new heuristic optimization algorithm is presented to solve the nonlinear optimization problems. The proposed algorithm utilizes a stochastic method to achieve the optimal point based on simplex techniques. A dual simplex is distributed stochastically in the search space to find the best optimal point. Simplexes share the best and worst vertices of one another to move better through search space. The proposed algorithm is applied to 25 well-known benchmarks, and its performance is compared with grey wolf optimizer (GWO), particle swarm optimization (PSO), Nelder-Mead simplex algorithm, hybrid GWO combined with pattern search (hGWO-PS), and hybrid GWO algorithm combined with random exploratory search algorithm (hGWO-RES). The numerical results show that the proposed algorithm, called stochastic dual simplex algorithm (SDSA), has a competitive performance in terms of accuracy and complexity.
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ISSN:2168-2267
2168-2275
2168-2275
DOI:10.1109/TCYB.2019.2931288