Two self-adaptive crossover operators for genetic programming
Advances in genetic programming
The scheduling of maintenance service
Discrete Applied Mathematics
Self-Adaptive Genetic Algorithm for Numeric Functions
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
A Combined Swarm Differential Evolution Algorithm for Optimization Problems
Proceedings of the 14th International conference on Industrial and engineering applications of artificial intelligence and expert systems: engineering of intelligent systems
Parameter Selection in Particle Swarm Optimization
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
Evolutionary Optimization Versus Particle Swarm Optimization: Philosophy and Performance Differences
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
The particle swarm optimization algorithm: convergence analysis and parameter selection
Information Processing Letters
Time Slot Allocation for Real-Time Messages with Negotiable Distance Constrains
RTAS '98 Proceedings of the Fourth IEEE Real-Time Technology and Applications Symposium
Stride Scheduling: Deterministic Proportional- Share Resource Management
Stride Scheduling: Deterministic Proportional- Share Resource Management
Journal of Scheduling
A discrete version of particle swarm optimization for flowshop scheduling problems
Computers and Operations Research
Fine-Tuning of Algorithms Using Fractional Experimental Designs and Local Search
Operations Research
Multiobjective optimization using dynamic neighborhood particle swarm optimization
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Don't push me! Collision-avoiding swarms
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Dissipative particle swarm optimization
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Extending particle swarm optimisers with self-organized criticality
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
A discrete particle swarm optimization algorithm for the no-wait flowshop scheduling problem
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
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
On the computation of all global minimizers through particle swarm optimization
IEEE Transactions on Evolutionary Computation
Solving the Response Time Variability Problem by means of Multi-start and GRASP metaheuristics
Proceedings of the 2008 conference on Artificial Intelligence Research and Development: Proceedings of the 11th International Conference of the Catalan Association for Artificial Intelligence
Frankenstein's PSO: a composite particle swarm optimization algorithm
IEEE Transactions on Evolutionary Computation
Using selection to improve quantum-behaved particle swarm optimisation
International Journal of Innovative Computing and Applications
Solving the response time variability problem by means of a psychoclonal approach
Journal of Heuristics
An intelligent augmentation of particle swarm optimization with multiple adaptive methods
Information Sciences: an International Journal
A branch and bound algorithm for the response time variability problem
Journal of Scheduling
Dynamic clustering using combinatorial particle swarm optimization
Applied Intelligence
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Particle swarm optimization (PSO) is an evolutionary metaheuristic inspired by the flocking behaviour of birds, which has successfully been used to solve several kinds of problems, although there are few studies aimed at solving discrete optimization problems. One disadvantage of PSO is the risk of a premature search convergence. To prevent this, we propose to introduce diversity into a discrete PSO by adding a random velocity. The degree of the introduced diversity is not static (i.e. preset before running PSO) but instead changes dynamically according to the heterogeneity of the population (i.e. if the search has converged or not). We solve the response time variability problem (RTVP) to test these two new ideas. The RTVP is an NP-hard combinatorial scheduling problem that has recently appeared in the literature. It 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. The most efficient algorithm for solving non-small instances of the RTVP published to date is a classical PSO algorithm, referred to by the authors as PSO-M1F. In this paper, we propose 10 discrete PSO algorithms for solving the RTVP: one based on the ideas described above (PSO-c"3dyn) and nine based on strategies proposed in the literature and adapted for solving a discrete optimization problem such as the RTVP. We compare all 11 PSO algorithms and the computational experiment shows that, on average, the best results obtained are due to our proposal of dynamic control mechanism for introducing diversity.