CLPSO

[thuchula6792]

[thuchula6792]

Many meta-heuristic algorithms have been introduced with underlying exploitation and exploration abilities. The particle swarm optimization (PSO), being a swarm-intelligence approach, emulates the movement or social behavior of a bird flock. The PSO constructs a set of particles in the population, where their positions are iteratively updated through the movement (velocity functions) learned from the global best particle. However, the premature local optima are often encountered by the standard PSO method as its social update components do not work sufficiently. Various new techniques have been incorporated within the original PSO to enhance its global search ability and overcome the local optimal pitfalls. Our recent work successfully applied an outstanding variant version of the PSO, called comprehensive learning particle swarm optimization (CLPSO) for the design of steel structures. In the CLPSO, the learning technique enables the cross positions between the sets of best swarm particles in each dimensional space leading to the likelihood of overcoming locally optimal searches and premature termination of the undesired non-optimal but feasible solutions. The proposed scheme follows suit the learning probability function to define the cooperative responses among swarm populations. [1] Van Thu Huynh, Tangaramvong S, Limkatanyu S, Xuan HN (2022). Two-phase ESO and comprehensive learning PSO method for structural optimization with discrete steel sections. Advances in Engineering Software. 167:103102. [2] Van Thu Huynh, Tangaramvong S, S. Muong, and P. T. Van (2022), Combined Gaussian local search and enhanced comprehensive learning PSO algorithm for size and shape optimization of truss structures, Buildings, 12-1976. [3] Van Thu Huynh, Tangaramvong, S., Do, B., Gao, W., & Limkatanyu, S. (2023). Sequential Most Probable Point Update Combining Gaussian Process and Comprehensive Learning PSO for Structural Reliability-Based Design Optimization. Reliability Engineering & System Safety, 109164.

[thuchula6792]

Many meta-heuristic algorithms have been introduced with underlying exploitation and exploration abilities. The particle swarm optimization (PSO), being a swarm-intelligence approach, emulates the movement or social behavior of a bird flock. The PSO constructs a set of particles in the population, where their positions are iteratively updated through the movement (velocity functions) learned from the global best particle. However, the premature local optima are often encountered by the standard PSO method as its social update components do not work sufficiently. Various new techniques have been incorporated within the original PSO to enhance its global search ability and overcome the local optimal pitfalls. Our recent work successfully applied an outstanding variant version of the PSO, called comprehensive learning particle swarm optimization (CLPSO) for the design of steel structures. In the CLPSO, the learning technique enables the cross positions between the sets of best swarm particles in each dimensional space leading to the likelihood of overcoming locally optimal searches and premature termination of the undesired non-optimal but feasible solutions. The proposed scheme follows suit the learning probability function to define the cooperative responses among swarm populations. [1] Van Thu Huynh, Tangaramvong S, Limkatanyu S, Xuan HN (2022). Two-phase ESO and comprehensive learning PSO method for structural optimization with discrete steel sections. Advances in Engineering Software. 167:103102. [2] Van Thu Huynh, Tangaramvong S, S. Muong, and P. T. Van (2022), Combined Gaussian local search and enhanced comprehensive learning PSO algorithm for size and shape optimization of truss structures, Buildings, 12-1976. [3] Van Thu Huynh, Tangaramvong, S., Do, B., Gao, W., & Limkatanyu, S. (2023). Sequential Most Probable Point Update Combining Gaussian Process and Comprehensive Learning PSO for Structural Reliability-Based Design Optimization. Reliability Engineering & System Safety, 109164.