A New Frequency Analysis Operator for Population Improvement in Genetic Algorithms to Solve the Job Shop Scheduling Problem

Abstract

Job Shop Scheduling is currently one of the most addressed planning and scheduling optimization problems in the field. Due to its complexity, as it belongs to the NP-Hard class of problems, meta-heuristics are one of the most commonly used approaches in its resolution, with Genetic Algorithms being one of the most effective methods in this category. However, it is well known that this meta-heuristic is affected by phenomena that worsen the quality of its population, such as premature convergence and population concentration in regions of local optima. To circumvent these difficulties, we propose, in this work, the use of a guidance operator responsible for modifying ill-adapted individuals using genetic material from well-adapted individuals. We also propose, in this paper, a new method of determining the genetic quality of individuals using genetic frequency analysis. Our method is evaluated over a wide range of modern GAs and considers two case studies defined by well-established JSSP benchmarks in the literature. The results show that the use of the proposed operator assists in managing individuals with poor fitness values, which improves the population quality of the algorithms and, consequently, leads to obtaining better results in the solution of JSSP instances. Finally, the use of the proposed operator in the most elaborate GA-like method in the literature was able to reduce its mean relative error from 1.395% to 0.755%, representing an improvement of 45.88%.

Genetic Algorithm for Feature Selection Applied to Financial Time Series Monotonicity Prediction: Experimental Cases in Cryptocurrencies and Brazilian Assets

“…In future developments, we intend to modify our material in order to make it effective in detecting values in financial time series and not just in detecting monotonicity. Furthermore, more elaborate metaheuristics, such as those based on massive local search [ 32 , 33 , 34 , 35 , 36 , 37 ], should be considered in feature selection.…”

Research on Production Scheduling Technology in Knitting Workshop Based on Improved Genetic Algorithm

“…Intelligent optimization algorithms can obtain the optimal solution with high probability in a reasonable time, and several common intelligent algorithms such as the evolutionary algorithm [9,15,16], artificial bee colony algorithm [17,18], rider optimization algorithm [19], GWO [20,21] and GA [22][23][24] have been widely applied. Pang, X. L [25] et al optimized the fireworks algorithm by introducing strategies such as non-linear radius and detecting the minimum explosion magnitude, Cauchy-Gaussian mixture operator and elite population selection.…”

“…The matching distances between parallel machines and the weights of the optimization objective were introduced to improve the genetic algorithm, and the effectiveness of the algorithm was verified. To overcome the premature convergence of the metaheuristic algorithm and the concentration of the population in the local optimization region, Viana, M. S [24] proposed a new method to determine individual genetic quality using genetic frequency analysis to improve the population quality of the algorithm, thus enhancing the effectiveness of the algorithm. Ni, F [30] proposed a multi-graph attributed reinforcement learning-based optimization algorithm (MGRO), which incorporates the reinforcement learning-based policy search approach with classic search operators and powerful multi-graph-based representation.…”

Mitigation of Make Span Time in Job Shop Scheduling Problem Using Gannet Optimization Algorithm

“…In order to improve production, various manufacturing tasks are allocated to machines at appropriate times. This is known as the job shop scheduling problem (JSSP), also termed as JSS [1]. The goal of JSSP is to determine the best timetable for distributing shared resources over time to competing tasks in order to decrease the total amount of time required to finish all tasks.…”