Abstract
In view of the shortcomings of the whale optimization algorithm (WOA), such as slow convergence speed, low accuracy, and easy to fall into local optimum, an improved whale optimization algorithm (IWOA) is proposed. First, the standard WOA is improved from the three aspects of initial population, convergence factor, and mutation operation. At the same time, Gaussian mutation is introduced. Then the nonfixed penalty function method is used to transform the constrained problem into an unconstrained problem. Finally, 13 benchmark problems were used to test the feasibility and effectiveness of the proposed method. Numerical results show that the proposed IWOA has obvious advantages such as stronger global search ability, better stability, faster convergence speed, and higher convergence accuracy; it can be used to effectively solve complex constrained optimization problems.
Highlights
Related Research WorkConstrained optimization problems (Cops) are a type of nonlinear programming problems that often occur in the fields of daily life and engineering applications. ere are usually two ways to solve this problem: deterministic algorithm and random algorithm [16]
In view of the shortcomings of the whale optimization algorithm (WOA), such as slow convergence speed, low accuracy, and easy to fall into local optimum, an improved whale optimization algorithm (IWOA) is proposed
As a swarm intelligence optimization algorithm, like DE, PSO, ACO, and other algorithms, they all have the shortcomings of slow convergence and easy to fall into local optimum. erefore, in practical applications, various improvements have been made to the standard algorithms, such as [6,7,8,9,10]. erefore, for the WOA algorithm, in recent years, many scholars have made a lot of improvements in improving algorithm convergence speed and optimization accuracy
Summary
Constrained optimization problems (Cops) are a type of nonlinear programming problems that often occur in the fields of daily life and engineering applications. ere are usually two ways to solve this problem: deterministic algorithm and random algorithm [16]. E random algorithm is a swarm intelligence optimization algorithm that has emerged in recent years; it has obtained a lot of research in solving constrained optimization problems. Long and Zhang [18] proposed an improved bat algorithm for solving Cops. An improved particle swarm optimization algorithm for solving Cops was proposed by Mi Yong and Gao [19]. Lei et al [20] proposed a new empire competition algorithm to solve the Cops and used the lexicographic method to simultaneously optimize the objective function of the problem and the degree of constraint violation. Long et al [21] proposed the firefly algorithm to solve the constrained optimization problem. Mohamed et al [23,24,25] proposed using an improved differential evolution algorithm to solve constrained optimization problems. S.t. hj(x) 0, j p + 1, p + 2, . . . , m li ≤ xi ≤ ui, i 1, 2, . . . , d, where f(x) is the objective function, gj(x) is the inequality constraint, hj(x) is the equality constraint, and li and ui are the upper and lower bounds of the variable xi, respectively
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