An agent based approach to site selection for wireless networks
Proceedings of the 2002 ACM symposium on Applied computing
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
The Cross Entropy Method: A Unified Approach To Combinatorial Optimization, Monte-carlo Simulation (Information Science and Statistics)
Scalable Optimization via Probabilistic Modeling: From Algorithms to Applications (Studies in Computational Intelligence)
Free search differential evolution
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
An investigation into the merger of stochastic diffusion search and particle swarm optimisation
Proceedings of the 13th annual conference on Genetic and evolutionary computation
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Stochastic Diffusion Search (SDS) is a population-based, naturally inspired search and optimization algorithm. It belongs to a family of swarm intelligence (SI) methods. SDS is based on direct (one-to-one) communication between agents. SDS has been successfully applied to a wide range of optimization problems. In this paper we consider the SDS method in the context of unconstrained continuous optimization. The proposed approach uses concepts from probabilistic algorithms to enhance the performance of SDS. Hence, it is named the Probabilistic SDS (PSDS). PSDS is tested on 16 benchmark functions and is compared with two methods (a probabilistic method and a SI method). The results show that PSDS is a promising optimization method that deserves further investigation.