Abstract
Sine Cosine Algorithm (SCA) is a newly proposed competent population-based metaheuristic, which has gained a multi-disciplinary interest in solving optimization problems. Like other metaheuristics, the performance of SCA is sensitive to the settings of its parameters and one such parameter is Population Size (PS). There is no one population size that fits all; problem uniqueness requires a matching strategy of parameter selection and adaptation. However, in standard SCA and its variants, PS is treated as a user-controlled parameter and no study has explored the effect of PS adaptation on SCA's performance. To fill this research gap, this study investigates and compares the impact of promising strategies for setting and controlling population size, from other metaheuristics, on the performance of the standard SCA and five of its variants in terms of fitness, run-time, and convergence characteristics. Finding the best PS setting for a metaheuristic is a challenging problem since it depends on both the nature of the algorithm used and the problem being solved. Leading PS adaptation techniques considered in this study are linear staircase reduction, iterative halving, reinitialization and incrementation, pulse wave, population diversity, and three parent crossover strategies. A classic set of 23 well-known benchmark functions has been utilized for a fixed number of evaluations to assess the impact of each PS adaptation strategy on the performance of SCA and its variants. Also, non-parametric Wilcoxon's rank sum test is performed to provide a comprehensive view of various PS adaptation strategies' performance with respect to each other in terms of fitness and run-time. Simulation results reveal that proper selection of PS adaptation strategy can further enhance the exploration and exploitation capabilities of SCA and its variants.
Highlights
Complex real-world problems in science, finance, engineering, and a multitude of other disciplines can be formulated as optimization problems
Different objective functions classified into three groups of benchmarks have been employed to assess the performance of Sine Cosine Algorithm (SCA) under diverse adaptation methods
WORKS The recently developed Sine Cosine Algorithm (SCA) has attracted much attention from diverse fields because of its competent performance when compared to other metaheuristics
Summary
Complex real-world problems in science, finance, engineering, and a multitude of other disciplines can be formulated as optimization problems. Population-based metaheuristic algorithms have been the dominant methods over the past few decades to find efficient solutions to challenging optimization problems within a reasonable time. Algorithms go through two cycles when generating solutions, namely, exploration (global search), and exploitation (local search) phases. While the adaptation of Population Size (PS) has been extensively studied in Differential Evolution (DE) [7], Genetic Algorithms (GA) [8], and Swarm Intelligence (SI) [9], no such work has been done for SCA. This work aims at filling this gap by examining different population size adaptation methods applied previously and study their impact on SCA’s performance along with some of its variants. Sub-section C discusses different PS adaptation methods borrowed from other optimization algorithms and highlights their significance. By keeping a proper balance between exploration and exploitation phases, SCA works towards finding a solution to an optimization problem.
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