PSO with Mixed Strategy for Global Optimization

Abstract

Particle swarm optimization (PSO) is an evolutionary algorithm for solving global optimization problems. PSO has a fast convergence speed and does not require the optimization function to be differentiable and continuous. In recent two decades, a lot of researches have been working on improving the performance of PSO, and numerous PSO variants have been presented. According to a recent theory, no optimization algorithm can perform better than any other algorithm on all types of optimization problems. Thus, PSO with mixed strategies might be more efficient than pure strategy algorithms. A mixed strategy PSO algorithm (MSPSO) which integrates five different PSO variants was proposed. In MSPSO, an adaptive selection strategy is used to adjust the probability of selecting different variants according to the rate of the fitness value change between offspring generated by each variant and the personal best position of particles to guide the selection probabilities of variants. The rate of the fitness value change is a more effective indicator of good strategies than the number of previous successes and failures of each variant. In order to improve the exploitation ability of MSPSO, a Nelder–Mead variant method is proposed. The combination of these two methods further improves the performance of MSPSO. The proposed algorithm is tested on CEC 2014 benchmark suites with 10 and 30 variables and CEC 2010 with 1000 variables and is also conducted to solve the hydrothermal scheduling problem. Experimental results demonstrate that the solution accuracy of the proposed algorithm is overall better than that of comparative algorithms.

Predictive current control method based on IPSO-ASMO for LIM

“…To optimize the performance of the LIM predictive control system, the adjusting of design parameters and gains of the observer is necessitated. Therefore, this study employs a particle swarm optimization algorithm to determine the optimal values of these parameters, namely 13 , , , g g m n .The range of values for each parameter must adhere to the requirements outlined in the Lyapunov stability analysis in the previous section [24,25,26].…”

An Intelligent Adaptive Neuro-Fuzzy Inference System for Modeling Time-Series Customer Satisfaction in Product Design

“…BOPSO is employed in ANFIS to search for the optimal inputs and simplify the model structure by removing unimportant inputs. PSO exhibits high stability and fast convergence speed, making it particularly well-suited for searching for the optimal solution [2]. Beiranvand and Mobasher-Kashani [3] discovered that the multi-objective PSO algorithm provides distinct advantages in generating association rules compared to other methods such as the multiobjective genetic algorithm, genetic association rules, and the multi-objective differential evolution algorithm.…”