Multiobjective Flexible Job Shop Scheduling Using A Modified Invasive Weed Optimization

Abstract

Recently, many studies are carried out with inspirations from ecological phenomena for developing optimization techniques. The new algorithm that is motivated by a common phenomenon in agriculture is colonization of invasive weeds. In this paper, a modified invasive weed optimization (IWO) algorithm is presented for optimization of multiobjective flexible job shop scheduling problems (FJSSPs) with the criteria to minimize the maximum completion time (makespan), the total workload of machines and the workload of the critical machine. IWO is a bio-inspired metaheuristic that mimics the ecological behaviour of weeds in colonizing and finding suitable place for growth and reproduction. IWO is developed to solve continuous optimization problems that's why the heuristic rule the Smallest Position Value (SPV) is used to convert the continuous position values to the discrete job sequences. The computational experiments show that the proposed algorithm is highly competitive to the state-of-the-art methods in the literature since it isable to find the optimal and best-known solutions on the instances studied.

Particle Swarm Optimization for Wireless Sensor Network Lifespan Maximization

“…The process of clustering sensor nodes into cover sets and scheduling them to maximize the lifespan of the network belongs to the NP-hard problems. This kind of problem can be solved using either exact methods, like linear programming and branch-bound or metaheuristics, namely genetic, invasive weed optimization, and Particle Swarm Optimization (PSO) algorithms [4][5][6][7][8]. The former approach requires an exponential computation time depending on the size of the problem to be solved, while the latter tries to obtain good solutions (not necessarily optimal) in a reasonable time.…”