Abstract
Particle swarm optimization (PSO) is a common metaheuristic algorithm. However, when dealing with practical engineering structure optimization problems, it is prone to premature convergence during the search process and falls into a local optimum. To strengthen its performance, combining several ideas of the differential evolution algorithm (DE), a dynamic probability mutation particle swarm optimization with chaotic inertia weight (CWDEPSO) is proposed. The main improvements are achieved by improving the parameters and algorithm mechanism in this paper. The former proposes a novel inverse tangent chaotic inertia weight and sine learning factors. Besides, the scaling factor and crossover probability are improved by random distributions, respectively. The latter introduces a monitoring mechanism. By monitoring the convergence of PSO, a developed mutation operator with a more reliable local search capability is adopted and increases population diversity to help PSO escape from the local optimum effectively. To evaluate the effectiveness of the CWDEPSO algorithm, 24 benchmark functions and two groups of engineering optimization experiments are used for numerical and engineering optimization, respectively. The results indicate CWDEPSO offers better convergence accuracy and speed compared with some well-known metaheuristic algorithms.
Highlights
An increasing number of fields, such as feature selection [1], artificial intelligence [2], and engineering structure design [3], have been faced with optimization problems due to the development of science and technology and industrialization level development
Researchers have invented many optimization algorithms, like the whale optimization (WOA) algorithm [4], biogeography-based optimization (BBO) [5], the sinecosine algorithm (SCA) [6], the moth-flame optimization (MFO) algorithm [7], ant colony optimization (ACO) [8], krill herd (KH) algorithm [9, 10], the artificial bee colony (ABC) [11], the gravitational search algorithm (GSA) [12], monarch butterfly optimization (MBO) [13, 14], earthworm optimization algorithm (EWA) [15], elephant herding optimization (EHO) [16, 17], moth search (MS) algorithm [18], slime mould algorithm (SMA) [19], Harris hawks optimization (HHO) [20], differential evolution (DE) algorithm, [21] and particle swarm optimization (PSO) [22, 23]. ese optimization algorithms are known as metaheuristic algorithms and are widely used in real life
The performance of CWDEPSO is evaluated by 24 benchmark functions that have been adopted widely in the field of numerical optimization methods [58, 70, 71], most of which belong to CEC2017 [72, 73], as shown in Tables 1–3. e experiment can be classified into two parts
Summary
An increasing number of fields, such as feature selection [1], artificial intelligence [2], and engineering structure design [3], have been faced with optimization problems due to the development of science and technology and industrialization level development. The engineering structure optimization problem is difficult to be solved by a specific algorithm because of extensive and complex constraints. Since the traditional optimization methods take too much time to solve these problems, making it difficult to find the optimal solution, people have started researching new optimization algorithms with a broader applicability. Erefore, PSO is widely used in practical engineering optimization. It is susceptible to premature convergence and falls into local
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