spherical search algorithm (SSA) calculates the spherical boundary and generates new solutions on it by two sub-populations jointly. Many researches have shown that SSA is a promising algorithm, but in some cases, the...
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spherical search algorithm (SSA) calculates the spherical boundary and generates new solutions on it by two sub-populations jointly. Many researches have shown that SSA is a promising algorithm, but in some cases, the fixed sub-population size causes it to be prone to search inadequately and easily falling into local optimum. In this paper, a new improved algorithm, named SSAP, is proposed to alleviate these problems. In SSAP, we propose a novel population control strategy to efficiently balance exploration and exploitation. This strategy adaptively adjusts the number of individuals in both sub-populations to improve the search performance of the algorithm. It is realized by adjusting the frequency of search patterns through a cumulative index. Comparative experiments conducted on a large number of benchmark functions show that SSAP significantly outperforms other state-of-the-art algorithms. Additionally, SSAP is used to solve real-world problems to further verify its validity. Finally, the search characteristics and population diversity of SSAP are analyzed.(c) 2022 Elsevier B.V. All rights reserved.
The spherical search algorithm (SS) generates novel solutions by partitioning the population and utilizing a sphericalsearch space. However, the fixed size of sub-populations leads to an accelerated convergence rate ...
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The spherical search algorithm (SS) generates novel solutions by partitioning the population and utilizing a sphericalsearch space. However, the fixed size of sub-populations leads to an accelerated convergence rate in SS, which often results in being trapped into local optima. This paper presents an advanced SS enhanced with a memory-based dynamic population scheduling system (SSDS). Building on the foundational SS framework, SSDS innovates with a dynamic population approach, utilizing a sub-population ratio record sequence to leverage historical data, and multiplexing historical population proportions reasonable improve the exploration behavior. As a result, SSDS dynamically balances exploration and exploitation throughout the search process. Preliminary results indicate that SSDS surpasses contemporary nine algorithms in IEEE congress on evolutionary computation (CEC) benchmark tests and exhibits promising application in 22 complex real-world problems. A closer analysis of the search performance and population diversity further highlights the effectiveness of the proposed SSDS.
In this paper, a new optimization algorithm called sphericalsearch (SS) is proposed to solve the bound-constrained non-linear global optimization problems. The main operations of SS are the calculation of spherical b...
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In this paper, a new optimization algorithm called sphericalsearch (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.
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