An enhanced Pareto-based artificial bee colony algorithm for the multi-objective flexible job-shop scheduling

Abstract

In this paper, an enhanced Pareto-based artificial bee colony (EPABC) algorithm is proposed to solve the multi-objective flexible job-shop scheduling problem with the criteria to minimize the maximum completion time, the total workload of machines, and the workload of the critical machine simultaneously. First, it uses multiple strategies in a combination way to generate the initial solutions as the food sources with certain quality and diversity. Second, exploitation search procedures for both the employed bees and the onlooker bees are designed to generate the new neighbor food sources. Third, crossover operators are designed for the onlooker bee to exchange useful information. Meanwhile, it uses a Pareto archive set to record the nondominated solutions that participate in crossover with a certain probability. To enhance the local intensification, a local search based on critical path is embedded in the onlooker bee phase, and a recombination and select strategy is employed to determine the survival of the individuals. In addition, population is suitably adjusted to maintain diversity in scout bee phase. By using Taguchi method of design of experiment, the influence of several key parameters is investigated. Simulation results based on the benchmarks and comparisons with some existing algorithms demonstrate the effectiveness of the proposed EPABC algorithm.

A Knowledge-Informed and Pareto-Based Artificial Bee Colony Optimization Algorithm for Multi-Objective Land-Use Allocation

“…However, as ABC has developed, several multi-objective variants such as vector evaluated ABC [35] and Pareto-based ABC [36][37][38][39] have been proposed. Vector evaluated ABC [35] lets different bee swarms optimize each objective concurrently and selects the solutions with the highest fitness in each swarm as forage bees to generate new solutions.…”

“…To avoid this limitation, Li et al [36] chose a random non-dominated solution among several randomly-selected solutions as the food source for onlooker bees. Wang et al [37] used tournament selection to choose the solutions with the best dominance rank and the largest crowding distance to attract onlooker bees. Akbari et al [39] used the roulette wheel strategy to assign each onlooker to a solution according to the probabilities determined by the number of dominated solutions for each food source.…”

Single Objective for an Integer Partial Flexible Open Shop Scheduling Problem Using Developed Ant Colony Optimization

“…In this best approach it has amazing considerable researches for late decades [2][3][4][5][6][7]. Open shop scheduling problem (OSP) is a branch of production scheduling and combinatorial optimization issues [8].…”

A Novel Memetic Algorithm Based on Decomposition for Multiobjective Flexible Job Shop Scheduling Problem

“…PSOs were used as global search algorithms in [33][34][35][36]. Besides, shuffled frog leaping [37] and artificial bee colony [38] were integrated with local search in related hybrid algorithms.…”