Cooperative Particle Swarm Optimization for the Delay Constrained Least Cost Path Problem

Abstract

This paper presents a particle swarm optimization (PSO) algorithm for solving the delay constrained least cost (DCLC) path problem, i.e., shortest path problem (SPP) with a delay constraint on the total “cost” of the optimal path. The proposed algorithm uses the principle of Lagrange relaxation based aggregated cost, where PSO and noising metaheuristic are used for minimizing the modified cost function. It essentially consists of two PSOs. The main PSO is basically a hybrid PSO-Noising metaheuristic algorithm for efficient global search for the minimization part of the DCLC-Lagrangian relaxation by finding multiple shortest paths between a source and a destination. The second/auxiliary PSO is used to obtain the optimal Lagrangian multiplier for solving the maximization part of the Lagrangian relaxation of the DCLC path problem. For the main PSO, a new path encoding/decoding scheme based on heuristics has been devised for representing the paths as particles. The comparative simulation results on several networks with random topologies illustrate the efficiency of the proposed hybrid algorithm for constrained shortest path computation.