Boosting crayfish algorithm based on halton adaptive quadratic interpolation and piecewise neighborhood for complex optimization problems

Abstract

The Crayfish Optimization Algorithm (CFish) is an innovative meta-heuristic approach that draws inspiration from the movements and behaviors of crayfish. CFish exhibits strong performance across many test sets and optimization issues, but it faces challenges with sluggish convergence, an uneven distribution between exploration and exploitation, and inadequate accuracy due to high-dimensional tasks. To tackle these concerns, this study presents an evolved version named QICFish, which integrates multiple sophisticated strategies to optimize performance. The CFish algorithm improves its population initialization by using the Halton sequence, resulting in a substantial enhancement of the exploration phase and an increase in population variety. Furthermore, the implementation of the adaptive quadratic interpolation approach enhances the algorithm's capacity to exploit its surroundings by eliminating individuals of worse quality and expediting the creation of solutions of superior quality. Furthermore, an adaptive mutation method is employed to improve the process of exploration and effectively locate more favorable areas inside the search space. Finally, the adaptive piecewise neighborhood strategy, enhances the pace of convergence and maintains a balanced transition between exploration and exploitation. Then, QICFish outperforms other cutting-edge algorithms across several dimensions of the CEC’17 and CEC’20 test sets, as shown by experimental comparisons. Furthermore, the efficacy and feasibility of the solution are confirmed by successfully addressing six intricate engineering problems and two truss topology optimization problems. The simulation findings demonstrate that QICFish exhibits robust competitive capabilities and shows great potential for engineering optimization challenges. Therefore, QICFish is a very efficient meta-heuristic approach for solving engineering optimization problems.