Abstract
In the last decade, many of the metaheuristic search methods have been proposed for solving tough optimization problems. Each of these algorithms uses its own learn-by-example mechanism in terms of “movement strategy” to evolve the candidate solutions. In this paper, a framework, called Search Manager, is proposed for hybridizing different learn-by-example methods in one algorithm, which is inspired by the organizational management system in which managers change their management method by viewing performance reduction in their managerial organization. The proposed framework is verified using standard benchmark functions and real-world optimization problems. Further, it is compared with some well-known heuristic search methods. The obtained results indicate not only the optimization capability of the proposed framework, but also its ability to obtain accurate solutions and to achieve higher convergence precision.
Highlights
In different areas of science such as industry, engineering, and management there are many complex problems, known as optimization problems, such that there is no exact algorithm to solve them in polynomial time
A statistical test called Wilcoxon rank-sum test [55], which is a nonparametric statistic test for the independent samples, is conducted on the experimental results at the 5% significance level to judge whether the obtained results from the proposed method are significantly different from the other algorithms and have not occurred by chance [56]
The cases are marked with “+/ ≈ /−” when the performance of Search Manager is significantly better than, equal to, and worse than the other test algorithms
Summary
In different areas of science such as industry, engineering, and management there are many complex problems, known as optimization problems, such that there is no exact algorithm to solve them in polynomial time. Due to this fact, to find a relatively optimal solution for these kinds of problems, in the past decades, a significant number of different optimization algorithms have been introduced by researchers. Optimization algorithms can be divided into two broad groups, deterministic and stochastic. Other advantages are the capability of escaping from a local optimum, good performance, and ease of implementation
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