Artificial Immune Systems: A New Computational Intelligence Paradigm
Artificial Immune Systems: A New Computational Intelligence Paradigm
Stride Scheduling: Deterministic Proportional- Share Resource Management
Stride Scheduling: Deterministic Proportional- Share Resource Management
Journal of Scheduling
Fine-Tuning of Algorithms Using Fractional Experimental Designs and Local Search
Operations Research
Introducing dynamic diversity into a discrete particle swarm optimization
Computers and Operations Research
Solving the Response Time Variability Problem by means of metaheuristics
Proceedings of the 2006 conference on Artificial Intelligence Research and Development
Parameter control in evolutionary algorithms
IEEE Transactions on Evolutionary Computation
Learning and optimization using the clonal selection principle
IEEE Transactions on Evolutionary Computation
A branch and bound algorithm for the response time variability problem
Journal of Scheduling
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The response time variability problem (RTVP) is a combinatorial scheduling problem that has recently appeared in the literature. This problem has a wide range of real life applications in fields such as manufacturing, hard real-time systems, operating systems and network environments. Originally, the RTVP occurs whenever products, clients or jobs need to be sequenced in such a way that the variability in the time between the instants at which they receive the necessary resources is minimized. Since the RTVP is hard to solve, heuristic techniques are needed for solving it. Three metaheuristic--multi-start, GRASP and PSO--algorithms proposed for solving the RTVP in two previous studies have been the most efficient to date in solving non-small instances of the RTVP. We propose solving the RTVP by means of a psychoclonal algorithm based approach. The psychoclonal algorithm inherits its attributes from Maslow's hierarchy of needs theory and the artificial immune system (AIS) approach, specifically the clonal selection principle. In this paper, we compare the proposed psychoclonal algorithm with the three aforementioned metaheuristic algorithms and show that, on average, the psychoclonal algorithm strongly improves on the results obtained.