Quadratic interpolation boosted black widow spider-inspired optimization algorithm with wavelet mutation

Meta-heuristic algorithms are effective in solving complex optimization problems with advantages of flexibility for coding, robustness and global optimization capability. An enhanced Black Widow Optimization called QIWBWO algorithm with three improvement strategies is proposed in this paper. At the beginning of search, the theory of good points set is used to obtain the better initial population, which helps the algorithm to quickly determine the correct search direction. Then, quadratic interpolation strategy is used to improve the solution accuracy and accelerate the convergence. Meanwhile, to avoid the algorithm falling into a local optimum, wavelet mutation is introduced to improve population diversity and helps the algorithm to search the global optimum rather than local optimums. The proposed BWO algorithm is compared with other different kinds of meta-heuristic algorithms on 25 traditional benchmark functions and CEC2017 competition suite. The statistical results show the improved BWO algorithm delivers better performance in accuracy, stability and convergence rate. Finally, QIWBWO also obtains the best results on four classical optimization problems in engineering application, which verifies its practicality and effectiveness. The source code of QIWBWO is publicly available in the supplementary material related to this article.