Multi-Agent Systems: An Introduction to Distributed Artificial Intelligence
Multi-Agent Systems: An Introduction to Distributed Artificial Intelligence
Evolutionary Optimization in Dynamic Environments
Evolutionary Optimization in Dynamic Environments
Designing evolutionary algorithms for dynamic optimization problems
Advances in evolutionary computing
A self-organizing random immigrants genetic algorithm for dynamic optimization problems
Genetic Programming and Evolvable Machines
KEEL: a software tool to assess evolutionary algorithms for data mining problems
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special Issue on Evolutionary and Metaheuristics based Data Mining (EMBDM); Guest Editors: José A. Gámez, María J. del Jesús, José M. Puerta
Reactive Search and Intelligent Optimization
Reactive Search and Intelligent Optimization
A study on diversity and cooperation in a multiagent strategy for dynamic optimization problems
International Journal of Intelligent Systems - Special Issue on Nature Inspired Cooperative Strategies for Optimization
An analysis of particle properties on a multi-swarm PSO for dynamic optimization problems
CAEPIA'09 Proceedings of the Current topics in artificial intelligence, and 13th conference on Spanish association for artificial intelligence
Optimization in dynamic environments: a survey on problems, methods and measures
Soft Computing - A Fusion of Foundations, Methodologies and Applications
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
Multiswarms, exclusion, and anti-convergence in dynamic environments
IEEE Transactions on Evolutionary Computation
An algorithm comparison for dynamic optimization problems
Applied Soft Computing
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The best performing methods for Dynamic Optimization Problems (DOPs) are usually based on a set of agents that can have different complexity (like solutions in Evolutionary Algorithms, particles in Particle Swarm Optimization, or metaheuristics in hybrid cooperative strategies). While methods based on low-complexity agents are widely applied in DOPs, the use of more ''intelligent'' agents has rarely been explored. This work focuses on this topic and more specifically on the use of cooperative strategies composed by trajectory-based search agents for DOPs. Within this context, we analyze the influence of the number of agents (cardinality) and their neighborhood sampling strategy on the performance of these methods. Using a low number of agents with distinct neighborhood sampling strategies shows the best results. This method is then compared versus state-of-the-art algorithms using as test bed the well-known Moving Peaks Benchmark and dynamic versions of the Ackley's, Griewank's and Rastrigin's functions. The results show that this configuration of the cooperative strategy is competitive with respect to the state-of-the-art methods.