A Hybrid Multi-Step Probability Selection Particle Swarm Optimization with Dynamic Chaotic Inertial Weight and Acceleration Coefficients for Numerical Function Optimization

Abstract

As a meta-heuristic algoriTthm, particle swarm optimization (PSO) has the advantages of having a simple principle, few required parameters, easy realization and strong adaptability. However, it is easy to fall into a local optimum in the early stage of iteration. Aiming at this shortcoming, this paper presents a hybrid multi-step probability selection particle swarm optimization with sine chaotic inertial weight and symmetric tangent chaotic acceleration coefficients (MPSPSO-ST), which can strengthen the overall performance of PSO to a large extent. Firstly, we propose a hybrid multi-step probability selection update mechanism (MPSPSO), which skillfully uses a multi-step process and roulette wheel selection to improve the performance. In order to achieve a good balance between global search capability and local search capability to further enhance the performance of the method, we also design sine chaotic inertial weight and symmetric tangent chaotic acceleration coefficients inspired by chaos mechanism and trigonometric functions, which are integrated into the MPSPSO-ST algorithm. This strategy enables the diversity of the swarm to be preserved to discourage premature convergence. To evaluate the effectiveness of the MPSPSO-ST algorithm, we conducted extensive experiments with 20 classic benchmark functions. The experimental results show that the MPSPSO-ST algorithm has faster convergence speed, higher optimization accuracy and better robustness, which is competitive in solving numerical optimization problems and outperforms a lot of classical PSO variants and well-known optimization algorithms.

A Hybrid Dynamic Probability Mutation Particle Swarm Optimization for Engineering Structure Design

“…24 benchmark test functions and two groups of engineering optimization experiments are adopted for testing the algorithm to evaluate its performance. e results suggest that the performance of CWDEPSO is better compared with PSO [22], DE [21], AIWCPSO [52], MPSPSO-ST [53], JADE [45], SinDE [54], and DEPSO [55] and five common metaheuristic algorithms GSA [12], ABC [11], MFO [7], SCA [6], and BBO [5]. Besides, it achieves the best performance in practical engineering optimization problems.…”

“…Correspondingly, a larger inertial weight is helpful for global exploration, and with small inertia weight, it is conducive to local exploration. Afterwards, after many numerous studies [53,58,59], it was found that the nonlinear ω is more helpful for algorithm enhancement. In addition, in this paper, we choose to use nonlinear ω and perform chaos treatment, aiming to enhance the disorder during iteration to enhance the population diversity of the algorithm in the late iteration.…”

“…Within the traditional PSO algorithm, the cognitive component is always equal to the social component, that is, c 1 � c 2 . However, many literatures [30,53,59] have demonstrated that the focus should be on the global exploration ability at the beginning of the iteration; i.e., the social component c 1 is larger, and the focus Mobile Information Systems should be on the local exploration ability at the later part of the iteration; i.e., the cognitive component c 2 is larger. In order to better balance the global exploration and local exploration ability in the iteration, in this paper, we use the sine function to optimize it so that c 1 gradually decreases and c 2 gradually increases.…”

Multi-Cluster Jumping Particle Swarm Optimization for Fast Convergence

“…The initial positions and velocities of the particles in a classical setting are initialized randomly in the search space. Whereas, there exists a number of studies in literature suggesting different strategies for inertia and acceleration coefficients [46]- [48].…”

Formulation of the Non-Parametric Value at Risk Portfolio Selection Problem Considering Symmetry

“…The swarm intelligence algorithm solves complex optimization problems by simulating the collective behavior of decentralized and self-organizing biological individuals in nature [24]. In recent years, a representative swarm intelligence algorithm called particle swarm optimization has been demonstrated to be an efficient optimization tool in solving non-convex complicated problems [25][26][27][28]. A comparison study of the PSO algorithm with a genetic algorithm in finding optimal portfolio solutions has been presented in [29] where several strategies for handling the constraints are designed.…”