Dindar ÖzOz, Dindar2025-10-062017095741740957-41741873-679310.1016/j.eswa.2016.09.0352-s2.0-84991259172https://www.scopus.com/inward/record.uri?eid=2-s2.0-84991259172&doi=10.1016%2Fj.eswa.2016.09.035&partnerID=40&md5=cb0fdd9074fb575270c3c29ff0503ba7https://gcris.yasar.edu.tr/handle/123456789/9741https://doi.org/10.1016/j.eswa.2016.09.035Allocating tasks to processors is a well-known NP-Hard problem in distributed computing systems. Due to the lack of practicable exact solutions it has been attracted by the researchers working on heuristic-based suboptimal search algorithms. With the recent inclusion of multiple objectives such as minimizing the cost maximizing the throughput and maximizing the reliability the problem gets even more complex and an efficient approximate method becomes more valuable. In this work I propose a new solution for the multi-objective task allocation problem. My solution consists in designing a problem-specific neighboring function for an existing metaheuristic algorithm that is proven to be successful in quadratic assignment problems. The neighboring function namely greedy reassignment with maximum release (GR-MR) provides a dynamic mechanism to switch the preference of the search between the exploration and exploitation. The experiments validate both that the quality of the solutions are close to the optimal and the proposed method performs significantly better comparing to three other metaheuristic algorithms. Neighboring functions being the common reusable components of metaheuristic algorithms GR-MR can also be utilized by other metaheuristic-based solutions in the future. © 2017 Elsevier B.V. All rights reserved.Englishinfo:eu-repo/semantics/closedAccessDistributed Systems, Metaheuristic Optimization Algorithms, Stochastic Search Algorithms, Task Allocation Problem, Combinatorial Optimization, Computational Complexity, Distributed Computer Systems, Heuristic Algorithms, Learning Algorithms, Packet Networks, Stochastic Systems, Distributed Computing Systems, Distributed Systems, Exploration And Exploitation, Meta Heuristic Algorithm, Meta-heuristic Optimizations, Quadratic Assignment Problems, Stochastic Search Algorithms, Task Allocation, OptimizationCombinatorial optimization, Computational complexity, Distributed computer systems, Heuristic algorithms, Learning algorithms, Packet networks, Stochastic systems, Distributed computing systems, Distributed systems, Exploration and exploitation, Meta heuristic algorithm, Meta-heuristic optimizations, Quadratic assignment problems, Stochastic search algorithms, Task allocation, OptimizationTask Allocation ProblemDistributed SystemsMetaheuristic Optimization AlgorithmsStochastic Search AlgorithmsArticle