Abstract
Swarm-based algorithm can successfully avoid the local optimal constraints, thus achieving a smooth balance between exploration and exploitation. Salp swarm algorithm (SSA), as a swarm-based algorithm on account of the predation behavior of the salp, can solve complex daily life optimization problems in nature. SSA also has the problems of local stagnation and slow convergence rate. This paper introduces an improved salp swarm algorithm, which improve the SSA by using the chaotic sequence initialization strategy and symmetric adaptive population division. Moreover, a simulated annealing mechanism based on symmetric perturbation is introduced to enhance the local jumping ability of the algorithm. The improved algorithm is referred to SASSA. The CEC standard benchmark functions are used to evaluate the efficiency of the SASSA and the results demonstrate that the SASSA has better global search capability. SASSA is also applied to solve engineering optimization problems. The experimental results demonstrate that the exploratory and exploitative proclivities of the proposed algorithm and its convergence patterns are vividly improved.
Highlights
The purpose of optimization is to find all possible results in a search space and to select the optimal solution according to conditions and parameters
The improved algorithm is compared with the Salp swarm algorithm (SSA) and several recently successful meta-heuristic algorithms, namely the moth flame optimization (MFO) [31], grey wolf optimization (GWO) and whale optimization algorithm (WOA)
The salp swarm algorithm is a meta-heuristic algorithm based on the predatory behavior of salp, which simulates the group of salp to join end-to-end in the form of a chain and move successively
Summary
The purpose of optimization is to find all possible results in a search space and to select the optimal solution according to conditions and parameters. The problems related to these fields are complex in nature and difficult to optimize, which is the basis for developing different meta-heuristic algorithms to find the optimal solution. The algorithm based on a single solution selects a candidate solution from all possible solution sets, and the selected candidate solution is evaluated repeatedly until the desired optimization result is achieved. The advantage of this approach is that it is faster to execute because of its lower complexity
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