A Multi Hybrid Genetic Algorithm for the Quadratic Assignment Problem
نویسندگان
چکیده
Quadratic assignment problem (QAP) is one of the hardest combinatorial optimization problems which can model many real life problems. Because of its theoretical and practical importance, QAP has attracted attention of many researchers. In this paper, a multi hybrid genetic algorithm for solving QAP is proposed. The key feature of our approach is the hybridization of three metaheuristics, tabu search, simulated annealing and ant system with genetic algorithm. These metaheuristics are used to create a good initial population and later to improve individuals in future generations. Our proposed approach is applied to a number of standard test problems and our computational results are compared with those of three metaheuristics when applied on the same problems alone. It is understood that our approach is one of best algorithms which deals with QAP.
منابع مشابه
A Honey Bee Algorithm To Solve Quadratic Assignment Problem
Assigning facilities to locations is one of the important problems, which significantly is influence in transportation cost reduction. In this study, we solve quadratic assignment problem (QAP), using a meta-heuristic algorithm with deterministic tasks and equality in facilities and location number. It should be noted that any facility must be assign to only one location. In this paper, first o...
متن کاملLocomotive assignment problem with train precedence using genetic algorithm
This paper aims to study the locomotive assignment problem which is very important for railway companies, in view of high cost of operating locomotives. This problem is to determine the minimum cost assignment of homogeneous locomotives located in some central depots to a set of pre-scheduled trains in order to provide sufficient power to pull the trains from their origins to their destinations...
متن کاملA hybrid genetic/immune strategy to tackle the multiobjective quadratic assignment problem
The Genetic Immune Strategy for Multiple Objective Optimization (GISMOO) is a hybrid algorithm for solving multiobjective problems. The performance of this approach has been assessed using a classical combinatorial multiobjective optimization benchmark: the multiobjective 0/1 knapsack problem (MOKP) [1] and two-dimensional unconstrained multiobjective problems (ZDT) [2]. This paper shows that t...
متن کاملAn Evolutionary Algorithm Based on a Hybrid Multi-Attribute Decision Making Method for the Multi-Mode Multi-Skilled Resource-constrained Project Scheduling Problem
This paper addresses the multi-mode multi-skilled resource-constrained project scheduling problem. Activities of real world projects often require more than one skill to be accomplished. Besides, in many real-world situations, the resources are multi-skilled workforces. In presence of multi-skilled resources, it is required to determine the combination of workforces assigned to each activity. H...
متن کاملApplying Genetic Algorithm to Dynamic Layout Problem
In today’s economy, manufacturing plants must be able to operate efficiently and respond quickly to changes in the product mix and demand.[1] Layout design has a significant impact on manufacturing efficiency. Initially, it was treated as a static decision but due to improvements in technology, it is possible to rearrange the manufacturing facilities in different scenarios. The Plant layout...
متن کاملHybrid Meta-heuristic Algorithm for Task Assignment Problem
Task assignment problem (TAP) involves assigning a number of tasks to a number of processors in distributed computing systems and its objective is to minimize the sum of the total execution and communication costs, subject to all of the resource constraints. TAP is a combinatorial optimization problem and NP-complete. This paper proposes a hybrid meta-heuristic algorithm for solving TAP in a ...
متن کامل