On the performance of the particle swarm optimization algorithm with various inertia weight variants for computing optimal control of a class of hybrid systems

Abstract

This paper presents an alternative and efficient method for solving the optimal control of single-stage hybrid manufacturing systems which are composed with two different categories: continuous dynamics and discrete dynamics. Three different inertia weights, a constant inertia weight (CIW), time-varying inertia weight (TVIW), and global-local best inertia weight (GLbestIW), are considered with the particle swarm optimization (PSO) algorithm to analyze the impact of inertia weight on the performance of PSO algorithm. The PSO algorithm is simulated individually with the three inertia weights separately to compute the optimal control of the single-stage hybrid manufacturing system, and it is observed that the PSO with the proposed inertia weight yields better result in terms of both optimal solution and faster convergence. Added to this, the optimal control problem is also solved through real coded genetic algorithm (RCGA) and the results are compared with the PSO algorithms. A typical numerical example is also included in this paper to illustrate the efficacy and betterment of the proposed algorithm. Several statistical analyses are carried out from which can be concluded that the proposed method is superior to all the other methods considered in this paper.

Nature inspired algorithm using particle swarm approach with variations in inertia weights for automatic test data generation based on dominance concepts

“…The GLbestIW strategy is projected in [9]. It is considered as a function of local and global estimations of the particles in various generations.…”

A Modified Particle Swarm Optimization Algorithm based on Self-Adaptive Acceleration Constants

“…There are lots of others strategies for variation of inertia weight like Adaptive Inertia Weight [9], Sigmoid Increasing Inertia Weight [10], Chaotic Inertia Weight [11], Oscillating Inertia Weight [12], Global-Local Best Inertia Weight [13], Simulated Annealing Inertia Weight [14], Exponent Decreasing Inertia [15], Natural Exponent Inertia Weight Strategy [16] Fine parametric tuning of evolutionary algorithms is very important aspect to improve accuracy and efficiency. Earlier approaches [6][7][8] were mainly focused on the variation of inertia weight to increase the efficiency of PSO.…”

“…Several authors [4][5][6][7][8][9][10][11][12][13][14][15][16][17][18] proposed different methods to achieve better accuracy and convergence. However, in this paper, we have proposed a Modified Particle Swarm Optimization (MPSO) Algorithm based on self-adaptive acceleration constants.…”

Particle swarm optimization of artificial-neural-network-based on-line trained speed controller for battery electric vehicle

“…• inertia weight (explorative factor) can decrease with the number of iterations or can be the function of particle performance [15], • constriction factor can be introduced (canonical PSO) as constant one or variable one [16], • x best global can be redefined to represent best solution found so far by particle's neighbourhood limited by the distance (the radius of the neighbourhood is infinite in the rule as presented in (2)) or by the fixed number of neighbours (particles closest in distance) [17], • particle can be attracted by every other particles in its neighbourhood (i.e. knowledge of x best i is distributed among all particles) [18], • boundary conditions can be introduced with the help of absorbing, reflecting or invisible walls [19].…”