Improved particle swarm optimizer for problems with variable evaluation time: Application to asymptotic homogenization

Abstract

This work presents a new particle swarm optimizer (PSO)-based metaheuristic algorithm designed to reduce overall computational cost without compromising optimization's precision in functions with variable evaluation time. The algorithm exploits the evaluation time gradient in addition to the convergence gradient attempting to reach the same convergence precision following a more economical path. The particle's newly incorporated time information is usually in contradiction to the past memories of best function evaluations thus degrading convergence. A technique is proposed in order to modulate the new cognitive input that consists of progressive reducing of its weight in order to confer the algorithm the appropriate time-convergence balance. Results show that the proposed algorithm not only provides computational economy, but also unexpectedly improves convergence per se due to a better exploration in the initial stages of optimization. Its application in asymptotic homogenization of a cracked poroelastic medium confirms its superior performance compared to a series of alternative optimization algorithms. The proposed algorithm improvement allows to extend the applicability of PSO and PSO-based algorithms to problems that were previously thought to be too computationally expensive for population-based approaches.

Numerical design optimization of real size complex steel space frame structures using a novel adaptive cuckoo search method

“…23 The improved versions of the standard metaheuristic algorithms have also come in sight over time to dispose the algorithmic shortcomings of standard ones, recently. [24][25][26][27] As instances from the current ones; for structural engineering field four modified versions of standard grasshopper optimization algorithm to solve large-scale real-size complex both truss and frame type steel structures are proposed, 28 the differential evolution with adaptive penalty method is used for structural multi-objective optimization, 29 the five different trusses are optimized via craziness based particle swarm optimization (CRPSO), which is an adaptive version of the particle swarm optimization (PSO), 30 and so forth. Furthermore, a developed metaheuristic algorithm may give very successful results in one design field, but may not show enough optimal solution performance in another design field.…”

“…The metaheuristics are utilized in many engineering fields such as design of head shape of a high‐speed train, 21 wave energy converters to maximize the power output, 22 and compact reactor radiation shielding design 23 . The improved versions of the standard metaheuristic algorithms have also come in sight over time to dispose the algorithmic shortcomings of standard ones, recently 24–27 . As instances from the current ones; for structural engineering field four modified versions of standard grasshopper optimization algorithm to solve large‐scale real‐size complex both truss and frame type steel structures are proposed, 28 the differential evolution with adaptive penalty method is used for structural multi‐objective optimization, 29 the five different trusses are optimized via craziness based particle swarm optimization (CRPSO), which is an adaptive version of the particle swarm optimization (PSO), 30 and so forth.…”