A New Genetic Algorithm with Agent-Based Crossover for Generalized Assignment Problem

Abstract

Generalized assignment problem (GAP) considers finding minimum cost assignment of n tasks to m agents provided each task should be assigned to one agent only. In this study, a new Genetic Algorithm (GA) with some new methods is proposed to solve GAPs. The agent-based crossover is based on the concept of dominant gene in genotype science and increases fertility rate of feasible solutions. The solutions are classified as infeasible, feasible and mature with reference to their conditions. The new local searches provide not only feasibility in high diversity but high profitability for the solutions. A solution is not given up through maturation-based replacement until it reaches its best. Computational results show that the agent-based crossover has much higher fertility rate compared to classical crossover. Also, the proposed GA creates either optimal or approximately optimal solutions.

Joint User Association and Beamforming in Integrated Satellite-HAPS-Ground Networks

“…Similarly, the joint user association and power assignment problem is addressed in [17]; please also see the references therein. The user-association subproblem considered in the current paper, however, involves a more intricate coupling of cross-mode cross-layered interference and HAPS connectivity constraints, and so the paper leverages techniques such as linear integer programming [18] and generalized assignment problems [19] to develop reasonable heuristics for dealing with the problem discrete intricacies.…”

“…2) Integer Linear Problem and Generalized Assignment Problem (ILP-GAP): To further improve the ILP-based solution, the paper goes one step beyond by proposing an additional heuristic that relies on maximizing an auxiliary interference-free function of the original objective function of the optimization problem (7). Such heuristic allows to use the ILP-based solution as an initial point to solve a generalized assignment problem of reasonable computational complexity; see [17], [19], [35] and references therein. The simulations results of our paper later illustrate the numerical prospect of our proposed heuristic ILP-GAP scheme, as it outperforms the classical user association techniques.…”

“…In this paper, we utilize the branch and bound techniques for its provable performance guarantees [18]. The solution of GAP is numerically manageable, yet strongly dependable on the initialization strategy [17], [19], [35]. Our paper, therefore, adopts the solution reached by solving the ILP ( 14) as the initial point, owing to its good numerical prospects.…”

Modeling and I-NSGA-III-VLC Solution of Aircraft Equipment Rotation and Echelon Usage under Uncertainty

“…Zhou et al [5] proposed a formation and control method of motorcycle working life echelon reserves using SAA to formulate a usage and repair plan of a motorcycle. For assignment problems, Dorterler [6] proposed a new genetic algorithm with agent-based crossover for the generalized assignment problem, but it is based on the idea that each task should be assigned to one agent only. Fu et al [7] studied the uncertain multi-objective assignment problem, in which the number of tasks each person undertakes is uncertain, but each task can only be undertaken by one person.…”