14,921 research outputs found
An island based hybrid evolutionary algorithm for optimization
This is a post-print version of the article - Copyright @ 2008 Springer-VerlagEvolutionary computation has become an important problem solving methodology among the set of search and optimization techniques. Recently, more and more different evolutionary techniques have been developed, especially hybrid evolutionary algorithms. This paper proposes an island based hybrid evolutionary algorithm (IHEA) for optimization, which is based on Particle swarm optimization (PSO), Fast Evolutionary Programming (FEP), and Estimation of Distribution Algorithm (EDA). Within IHEA, an island model is designed to cooperatively search for the global optima in search space. By combining the strengths of the three component algorithms, IHEA greatly improves the optimization performance of the three basic algorithms. Experimental results demonstrate that IHEA outperforms all the three component algorithms on the test problems.This work was supported by the Engineering and Physical Sciences Research Council (EPSRC) of UK under Grant EP/E060722/1
Multimodal estimation of distribution algorithms
Taking the advantage of estimation of distribution algorithms (EDAs) in preserving high diversity, this paper proposes a multimodal EDA. Integrated with clustering strategies for crowding and speciation, two versions of this algorithm are developed, which operate at the niche level. Then these two algorithms are equipped with three distinctive techniques: 1) a dynamic cluster sizing strategy; 2) an alternative utilization of Gaussian and Cauchy distributions to generate offspring; and 3) an adaptive local search. The dynamic cluster sizing affords a potential balance between exploration and exploitation and reduces the sensitivity to the cluster size in the niching methods. Taking advantages of Gaussian and Cauchy distributions, we generate the offspring at the niche level through alternatively using these two distributions. Such utilization can also potentially offer a balance between exploration and exploitation. Further, solution accuracy is enhanced through a new local search scheme probabilistically conducted around seeds of niches with probabilities determined self-adaptively according to fitness values of these seeds. Extensive experiments conducted on 20 benchmark multimodal problems confirm that both algorithms can achieve competitive performance compared with several state-of-the-art multimodal algorithms, which is supported by nonparametric tests. Especially, the proposed algorithms are very promising for complex problems with many local optima
Nonlinear system identification and control using state transition algorithm
By transforming identification and control for nonlinear system into
optimization problems, a novel optimization method named state transition
algorithm (STA) is introduced to solve the problems. In the proposed STA, a
solution to a optimization problem is considered as a state, and the updating
of a solution equates to a state transition, which makes it easy to understand
and convenient to implement. First, the STA is applied to identify the optimal
parameters of the estimated system with previously known structure. With the
accurate estimated model, an off-line PID controller is then designed optimally
by using the STA as well. Experimental results have demonstrated the validity
of the methodology, and comparisons to STA with other optimization algorithms
have testified that STA is a promising alternative method for system
identification and control due to its stronger search ability, faster
convergence rate and more stable performance.Comment: 20 pages, 18 figure
Bat Algorithm: Literature Review and Applications
Bat algorithm (BA) is a bio-inspired algorithm developed by Yang in 2010 and
BA has been found to be very efficient. As a result, the literature has
expanded significantly in the last 3 years. This paper provides a timely review
of the bat algorithm and its new variants. A wide range of diverse applications
and case studies are also reviewed and summarized briefly here. Further
research topics are also discussed.Comment: 10 page
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