Abstract
Sine cosine algorithm (SCA) is a new meta-heuristic approach suggested in recent years, which repeats some random steps by choosing the sine or cosine functions to find the global optimum. SCA has shown strong patterns of randomness in its searching styles. At the later stage of the algorithm, the drop of diversity of the population leads to locally oriented optimization and lazy convergence when dealing with complex problems. Therefore, this paper proposes an enriched SCA (ASCA) based on the adaptive parameters and chaotic exploitative strategy to alleviate these shortcomings. Two mechanisms are introduced into the original SCA. First, an adaptive transformation parameter is proposed to make transformation more flexible between global search and local exploitation. Then, the chaotic local search is added to augment the local searching patterns of the algorithm. The effectiveness of the ASCA is validated on a set of benchmark functions, including unimodal, multimodal, and composition functions by comparing it with several well-known and advanced meta-heuristics. Simulation results have demonstrated the significant superiority of the ASCA over other peers. Moreover, three engineering design cases are employed to study the advantage of ASCA when solving constrained optimization tasks. The experimental results have shown that the improvement of ASCA is beneficial and performs better than other methods in solving these types of problems.
Highlights
IntroductionIn the field of engineering, optimization algorithms have always been considered as some core stochastic methods [1], which often perform better than the traditional descent-based approach [2]
N is the number of populations; dim represents a dimension; MaxFEs represents the maximum number of function evaluations; Flod is the number of random runs. e parameters in Table 1 are used in the simulation experiments in Sections 4.3 and 4.4. e simulation experiment in Section 4.2 only changes the dimensions, and the other parameters are set the same
We will describe in detail the 31 benchmark functions which were used to test the performance of an enriched SCA (ASCA). e formulas of the functions are presented in Table 2, where Dim is the dimension, Range means the limit of the search space, and F represents the optimal solution
Summary
In the field of engineering, optimization algorithms have always been considered as some core stochastic methods [1], which often perform better than the traditional descent-based approach [2]. Many researchers have worked to develop new intelligent optimization algorithms to solve complex engineering cases [3,4,5,6,7,8,9,10,11,12,13,14,15,16]. Different from the algorithms above, the sine cosine algorithm (SCA) [30] is a new group of intelligence optimization algorithms proposed by Mirjalili in 2016. SCA does not have a complex mathematical model like many other algorithms, and its updating formulas rely on sine and cosine. Ere are only four parameters included in the update formula.
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