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The spherical search algorithm for bound-constrained global optimization problems
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AI
DOI：10.1016/j.asoc.2019.105734.png)
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Abstract
En 中文
In this paper, a new optimization algorithm called Spherical Search (SS) is proposed to solve the bound-constrained non-linear global optimization problems. The main operations of SS are the calculation of spherical boundary and generation of new trial solution on the surface of the spherical boundary. These operations are mathematically modeled with some more basic level operators: Initialization of solution, greedy selection and parameter adaptation, and are employed on the 30 black-box bound constrained global optimization problems. This study also analyzes the applicability of the proposed algorithm on a set of real-life optimization problems. Meanwhile, to show the robustness and proficiency of SS, the obtained results of the proposed algorithm are compared with the results of other well-known optimization algorithms and their advanced variants: Particle Swarm Optimization (PSO), Differential Evolution (DE), and Covariance Matrix Adapted Evolution Strategy (CMA-ES). The comparative analysis reveals that the performance of SS is quite competitive with respect to the other peer algorithms. (C) 2019 Published by Elsevier B.V.
Keywords:
Spherical search algorithm
Real-life optimization problems
Bound constrained optimization problem
Optimization algorithm
Global optimization
Journal
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6.6
Papers:
1.4W
Citations:
4.8W
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