A Genetic Algorithm Meta-Heuristic for a Generalized Quadratic Assignment Problem
The generalized quadratic assignment problem (GQAP) is one of the hardest problems to solve in the operations research area. The GQAP addressed in this work is defined as the task of minimizing the assignment and transportation costs of assigning a set of facilities to a set of locations. The facilities have different space requirements, and the locations have different space capacities. Multiple facilities can be assigned to each location if the space capacity is not violated. In this work, three instances of GQAP in different situations are presented. Then, a genetic algorithm is developed to solve the GQAP instances. Finally, the local neighborhood search with the steepest descend strategy is constructed and applied to the final solution obtained by the GA, and the final solution is compared with the best solution found by MPL/CPLEX software and reference papers. The results show that the developed GA heuristic is effective for solving the GQAP.
Code (1)
Methods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
A Comparative Study of Meta-heuristic Algorithms for Solving Quadratic Assignment Problem
Quadratic Assignment Problem (QAP) is an NP-hard combinatorial optimization problem, therefore, solving the QAP requires applying one or more of the meta-heuristic algorithms. This paper presents a comparative study betw…
Combinatorial OptimizationPerformance Analysis of Meta-heuristic Algorithms for a Quadratic Assignment Problem
A quadratic assignment problem (QAP) is a combinatorial optimization problem that belongs to the class of NP-hard ones. So, it is difficult to solve in the polynomial time even for small instances. Research on the QAP ha…
Combinatorial OptimizationResource allocation using metaheuristic search
This research is focused on solving problems in the area of software project management using metaheuristic search algorithms and as such is research in the field of search based software engineering. The main aim of thi…
ManagementSchedulingAn Adaptive Genetic Algorithm for Solving N-Queens Problem
In this paper a Metaheuristic approach for solving the N-Queens Problem is introduced to find the best possible solution in a reasonable amount of time. Genetic Algorithm is used with a novel fitness function as the Meta…
Runtime Analysis of a Multi-Valued Compact Genetic Algorithm on Generalized OneMax
A class of metaheuristic techniques called estimation-of-distribution algorithms (EDAs) are employed in optimization as more sophisticated substitutes for traditional strategies like evolutionary algorithms. EDAs general…
Evolutionary Algorithms