MSFPSO: Multi-algorithm integrated particle swarm optimization with novel strategies for solving complex engineering design problems

Particle swarm optimization (PSO) is considered among the best seminal meta-heuristic algorithms,boasting merits of minimal parameter requirements, straightforward implementation, and highly accelerated convergence capacity, lower computational complexity, etc. Nevertheless, it also has drawbacks, for instance, it tends to converge prematurely at local optima, lack of diversity, and low accuracy. In order to effectively overcome these shortcomings, this paper presents a multi-strategy fusion enhanced PSO called MSFPSO algorithm. Firstly,It motivated by the black-winged kite algorithm, a migration mechanism based on Cauchy's variation is introduced. This mechanism contributes to the efficiency and effectiveness of the algorithm in exploiting the present search area. Also, it effectively balances the dynamics relationship between exploration and exploitation, boosting the algorithm's global and local search capabilities.Second, a jointopposition selection strategy is introduced for expanding the solution search range. Our approach is designed to avoid getting stuck in local optima. Specifically, selective opposition obtains the proximity dimension of a candidate solution through a linearly decreasing threshold. Dynamic opposition further extends the process of investigating the solution space. The algorithm is fully incorporated with the differential creative search algorithm for dual-strategy scenarios to enhance the performance of the decision-making effectiveness, population diversity, exploitation capability of the PSO. Finally, an attraction-rejection optimization strategy is introduced to further obtain a good exploitation-exploration balance capability and avoid stagnation of the algorithm. In addition, the comparison results with eight advanced optimization algorithms and six improved particle swarm optimization algorithms on CEC2020 test sets, and the statistical analysis was conducted by Wilcoxon rank sum test. It illustrate the features of the MSFPSO developed within this research strong competitiveness. The convergence of the algorithm was verified at maximum iterations of 10000 on the CEC2017 test set. Meanwhile, the experimental outcomes of applying MSFPSO to 50 practical engineering design challenges prove its effectiveness and strong applicability. The test results and numerical computations manifest that the MSFPSO algorithm with strong competitiveness will become a preferred class of meta-heuristic algorithms to tackle issues within the realm of engineering optimization.