Swarm intelligence: from natural to artificial systems
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Introduction to Evolutionary Computing
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Black-box optimization benchmarking for noiseless function testbed using particle swarm optimization
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Black-box optimization benchmarking for noiseless function testbed using PSO_bounds
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Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
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Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
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Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
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Proceedings of the 12th annual conference on Genetic and evolutionary computation
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Fast bio-inspired computation using a GPU-based systemic computer
Parallel Computing
Engineering Optimization: An Introduction with Metaheuristic Applications
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New inspirations in swarm intelligence: a survey
International Journal of Bio-Inspired Computation
Evaluation of parallel particle swarm optimization algorithms within the CUDATM architecture
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The differential ant-stigmergy algorithm
Information Sciences: an International Journal
Enhancing data parallelism for Ant Colony Optimization on GPUs
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Many promising optimization algorithms for solving numerical optimization problems come from population-based metaheuristics. A few of them are based on Swarm-Intelligence Algorithms, which are inspired by the collective behavior of social organisms. One of the most successful of such algorithms is the Differential Ant-Stigmergy Algorithm (DASA), which uses stigmergy, a method of communication in emergent systems where the individual parts (artificial ants) of the system communicate with one another by modifying their local environment (pheromone intensity). The main characteristic of the DASA is its underlying structure (pheromone graph) that uses discrete steps to move through a continuous search space. As a consequence of this the search-space movement is in some way limited and the algorithm's time/space complexity is increased. In order to overcome the problem an improved algorithm called the Continuous Differential Ant-Stigmergy Algorithm (CDASA) is proposed and then benchmarked on standard benchmark functions. This benchmarking showed that the CDASA performs better than the DASA, especially at lower dimensions, that its time/space complexity is decreased, and that the algorithm code is simplified. As such, the CDASA is more suitable for parallel implementations on General-Purpose Graphic Processing Units. Compared to the Swarm-Intelligence Algorithms presented in this paper, the CDASA is the best-performing algorithm and competitive to the state-of-the-art algorithms belonging to different metaheuristic approaches.