Multi-strategy-based adaptive sine cosine algorithm for engineering optimization problems

Sine Cosine Algorithm(SCA) is a population-based optimization algorithm, to find the optimal solution. However, SCA has problems such as premature convergence, insufficient solution precision for high-dimensional functions, and slow convergence speed. To solve the problems above, this paper proposes a multi-strategy-based Adaptive Sine Cosine Algorithm (ASCA). Firstly, a more uniform initial population is generated by the Halton sequence so that the initial population covers the entire search space to maintain the diversity of the initial population. Secondly, the adaptive grading strategy is adopted to sort according to the fitness value, and the population dynamics are divided into 4 grades: excellent, good, medium and poor. For the purpose of improving the convergence accuracy of the algorithm and enhancing the ability to jump out of the local optimum, hybrid mutation and elite guidance methods are applied to different levels of populations for perturbing mutations. Finally, in order to improve the convergence speed of the algorithm, a dynamic opposition-based learning global search strategy is proposed. The ASCA is tested on a set of 20 functions in low- dimensional and high-dimensional, and the improved algorithm is compared with Particle Swarm Optimization (PSO), Backtracking Search Algorithm(BSA), Genetic Algorithm(GA)and other improved Sine Cosine Algorithms. The results show the improved convergence accuracy and speed of the ASCA. Moreover, the ASCA proposed in this paper is applied to engineering optimization design. The solution results show that the ASCA is better than other algorithms in superiority-seeking ability, and can effectively solve the optimization problems in engineering.