3 research outputs found
A Multi-Swarm Cellular PSO based on Clonal Selection Algorithm in Dynamic Environments
Many real-world problems are dynamic optimization problems. In this case, the
optima in the environment change dynamically. Therefore, traditional
optimization algorithms disable to track and find optima. In this paper, a new
multi-swarm cellular particle swarm optimization based on clonal selection
algorithm (CPSOC) is proposed for dynamic environments. In the proposed
algorithm, the search space is partitioned into cells by a cellular automaton.
Clustered particles in each cell, which make a sub-swarm, are evolved by the
particle swarm optimization and clonal selection algorithm. Experimental
results on Moving Peaks Benchmark demonstrate the superiority of the CPSOC its
popular methods.Comment: 5 pages, 3 figures, conference pape
Tracking Extrema in Dynamic Environment using Multi-Swarm Cellular PSO with Local Search
Many real-world phenomena can be modelled as dynamic optimization problems.
In such cases, the environment problem changes dynamically and therefore,
conventional methods are not capable of dealing with such problems. In this
paper, a novel multi-swarm cellular particle swarm optimization algorithm is
proposed by clustering and local search. In the proposed algorithm, the search
space is partitioned into cells, while the particles identify changes in the
search space and form clusters to create sub-swarms. Then a local search is
applied to improve the solutions in the each cell. Simulation results for
static standard benchmarks and dynamic environments show superiority of the
proposed method over other alternative approaches.Comment: 8 pages, 3 figure
[EEE/OSA/[APR International Conference on [nfonnatics, Electronics & Vision A Multi-Swarm Cellular PSO based on Clonal Selection Algorithm in Dynamic Environments
Abstract- Many real-world problems are dynamic optimization problems. In this case, the optima in the environment change dynamically. Therefore, traditional optimization algorithms disable to track and find optima. In this paper, a new multiswarm cellular particle swarm optimization based on clonal selection algorithm (CPSOC) is proposed for dynamic environments. In the proposed algorithm, the search space is partitioned into cells by a cellular automaton. Clustered particles in each cell, which make a sub-swarm, are evolved by the particle swarm optimization and clonal selection algorithm. Experimental results on Moving Peaks Benchmark demonstrate the superiority of the CPSOC its popular methods. Keywords- dynamic environment; multi swarm cellular pso; cellular automata; clonal selection algorithm. system [9-10] and ant colony optimization were presented fo