| Summary: | Salp swarm algorithm is a new meta-heuristic algorithm which has excellent advantages for solving the multidimensional optimization problem. In this paper, a hybrid model assisted evolutionary algorithm for solving engineering design optimization problems is proposed and investigated. The purpose of the optimizer is to improve the potential shortcomings of the basic salp swarm optimization, including trapping in local or deceptive optima easily. On the one hand, quantum behavior can increase an individual's searchability, which can promote the overall optimization trend. On the other hand, the elite opposition-based learning strategy is used to enhance the diversity of the population. Besides, mutation mechanism is introduced to prevent the individuals of the population from being in stagnation behavior. The proposed quantum-behaved and wavelet mutation salp swarm algorithm (QSSA) is applied on twenty-three benchmark functions and three basic constrained engineering problems. Experimental results demonstrate that the algorithm has excellent solution quality, and it can overcome the defect of the low convergence rate.
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