Analysis of new niching genetic algorithms for finding multiple solutions in the job shop scheduling

Abstract

In this paper the performance of the most recent multi-modal genetic algorithms (MMGAs) on the Job Shop Scheduling Problem (JSSP) is compared in term of efficacy, multi-solution based efficacy (the algorithm's capability to find multiple optima), and diversity in the final set of solutions. The capability of Genetic Algorithms (GAs) to work on a set of solutions allows us to reach different optima in only one run. Nevertheless, simple GAs are not able to maintain different solutions in the last iteration, therefore reaching only one local or global optimum. Research based on the preservation of the diversity through MMGAs has provided very promising results. These techniques, known as niching methods or MMGAs, allow not only to obtain different multiple global optima, but also to preserve useful diversity against convergence to only one solution (the usual behaviour of classical GAs). In previous works, a significant difference in the performance among methods was found, as well as the importance of a suitable parametrization. In this work classic methods are compared to the most recent MMGAs, grouped in three classes (sharing, clearing and species competition), for JSSP. Our experimental study found that those new MMGAs which have a certain type of replacement process perform much better (in terms of highest efficacy and multi-solution based efficacy) than classical MMGAs which do not have this type of process.

Seeking Multiple Solutions: An Updated Survey on Niching Methods and Their Applications

“…Perez et al [154], [155] represents one of the very few studies on JSSP with a focus on identifying multiple solutions. JSSP are typically multi-modal, presenting an ideal case for applying niching methods.…”

Potential of Particle Swarm Optimization and Genetic Algorithms for FIR Filter Design

“…Modifications to the original PSO essentially concern the parameter improvement and population structure. They have in common the goal of adding more flexibility, thus contributing to prevent the above problems [28,29]. In this section, we focus on two PSO versions that are in line with the most recent advances in the evolutionary field.…”

Research of job-shop scheduling problem based on improved crossover strategy genetic algorithm

“…The execution time of the individual is the main basis for judging the individual merits of the required execution time is the longer, the worse of the program, the individual's fitness is also lower [8]. The warehouse transport vehicles match multiple parallel execution of tasks of the processor, in order to achieve optimal utilization of resources, the scheduler wants as much as possible use of the vehicle [10], the car transporters are assigned task to achieve vehicle resources full use of [9].…”