A novel multi-objective bacteria foraging optimization algorithm (MOBFOA) for multi-objective scheduling - Kaur, Mandeep; Kadam, Sanjay (Applied soft computing, 2018)
•We propose a novel multi-objective bacteria foraging algorithm (MOBFOA).•An adaptive chemotaxis process enables MOBFOA to converge to near-optimal solution.•A new bacteria fitness assignmnet strategy and a new bacteria selection method is proposed.•A comparative study between MOBFOA and other stochastic algorithms has been performed.•The proposed MOBFOA is a viable tool to handle multi-objective scheduling problem. In grid and cloud computing environment, many users compete for the resources, so the schedule should be generated in the shortest possible time. To address this problem, there have been several research initiatives to use evolutionary and swarm based algorithms to find near-optimal scheduling solutions. The state-of-the-art evolutionary algorithms for handling single/multi-criteria scheduling of m jobs on n resources are still evolving, with efforts aimed at reducing their space/time complexity, maintaining diversity in the population and directing the search towards the true Pareto-optimal solutions. In this paper, we have proposed a multi-objective bacteria foraging optimization algorithm (MOBFOA) to address these objectives. We have attempted to modify the original BFOA to handle the multi-objective scheduling problems using Pareto-optimal front approach. The modification is in terms of selecting bacteria positions from both the dominant as well as non-dominant fronts to obtain diversity in the solutions obtained. The accuracy and speed of the convergence of the BFOA has been improved by introducing adaptive step size in chemotactic step. The proposed MOBFOA uses new fitness assignment method and bacteria selection procedure for simultaneous optimization of multiple objectives, where each solution evaluation is computationally expensive. This paper focuses on the scheduling of independent jobs considering multi-objective trade-offs among the objective functions desired by the users in grid/cloud environment. The performance of the proposed MOBFOA is discussed in terms of convergence towards the Pareto-optimal front and distribution of solutions in the search space. The paper also provides a comparative study of the results obtained by the proposed MOBFOA with other stochastic optimization algorithms, namely, the non-dominated sorting genetic algorithm-II (NSGA-II) and optimised multi-objective particle swarm optimization (OMOPSO).