A Novel SCA-VPPSO Algorithm for Global Optimization Problems and Its Engineering Application
The sine cosine algorithm and the velocity paused particle swarm optimization algorithm are two highly effective metaheuristic algorithms employed for addressing continuous global optimization problems.However,when applied to practical scenarios,these algorithms consistently encounter the challenge of escaping local minima.Therefore,we propose the SCA-VPPSO algorithm,a novel hybrid search algorithm designed for addressing continuous global optimization problems.Based on the search framework of velocity paused particle swarm algorithm,the sine-cosine search operator is transformed from the original full-dimensional update strategy to a partial dimension update strategy,which is used in development and exploration,and integrates with the local search behavior of velocity paused particle swarm algorithm to form a two-mode local exploration mode.The hybrid SCA-VPPSO algorithm can balance local utilization and global exploration more effectively,thus enhancing the ability of the algorithm to escape the local minimum and obtain better results.The performance evaluation of the proposed algorithm,in conjunction with the sine cosine algorithm,velocity paused particle swarm optimization algorithm,and two recently published exemplary algorithms,was carried out on both the CEC2019 test set and an engineering practical application.The findings demonstrated a notable enhancement in the optimization performance of the proposed algorithm,thereby broadening its potential applications and presenting a novel hybrid search approach for the advancement of metaheuristic algorithms.
global optimizationparticle swarm optimization(PSO)sine cosine algorithm(SCA)metaheuristic algorithmengineering application
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