1,324 research outputs found
Adaptive particle swarm optimization
An adaptive particle swarm optimization (APSO) that features better search efficiency than classical particle swarm optimization (PSO) is presented. More importantly, it can perform a global search over the entire search space with faster convergence speed. The APSO consists of two main steps. First, by evaluating the population distribution and particle fitness, a real-time evolutionary state estimation procedure is performed to identify one of the following four defined evolutionary states, including exploration, exploitation, convergence, and jumping out in each generation. It enables the automatic control of inertia weight, acceleration coefficients, and other algorithmic parameters at run time to improve the search efficiency and convergence speed. Then, an elitist learning strategy is performed when the evolutionary state is classified as convergence state. The strategy will act on the globally best particle to jump out of the likely local optima. The APSO has comprehensively been evaluated on 12 unimodal and multimodal benchmark functions. The effects of parameter adaptation and elitist learning will be studied. Results show that APSO substantially enhances the performance of the PSO paradigm in terms of convergence speed, global optimality, solution accuracy, and algorithm reliability. As APSO introduces two new parameters to the PSO paradigm only, it does not introduce an additional design or implementation complexity
Improved sampling of the pareto-front in multiobjective genetic optimizations by steady-state evolution: a Pareto converging genetic algorithm
Previous work on multiobjective genetic algorithms has been focused on preventing genetic drift and the issue of convergence has been given little attention. In this paper, we present a simple steady-state strategy, Pareto Converging Genetic Algorithm (PCGA), which naturally samples the solution space and ensures population advancement towards the Pareto-front. PCGA eliminates the need for sharing/niching and thus minimizes heuristically chosen parameters and procedures. A systematic approach based on histograms of rank is introduced for assessing convergence to the Pareto-front, which, by definition, is unknown in most real search problems.
We argue that there is always a certain inheritance of genetic material belonging to a population, and there is unlikely to be any significant gain beyond some point; a stopping criterion where terminating the computation is suggested. For further encouraging diversity and competition, a nonmigrating island model may optionally be used; this approach is particularly suited to many difficult (real-world) problems, which have a tendency to get stuck at (unknown) local minima. Results on three benchmark problems are presented and compared with those of earlier approaches. PCGA is found to produce diverse sampling of the Pareto-front without niching and with significantly less computational effort
Fitness sharing and niching methods revisited
Interest in multimodal optimization function is expanding rapidly since real-world optimization problems often require the location of multiple optima in the search space. In this context, fitness sharing has been used widely to maintain population diversity and permit the investigation of many peaks in the feasible domain. This paper reviews various strategies of sharing and proposes new recombination schemes to improve its efficiency. Some empirical results are presented for high and a limited number of fitness function evaluations. Finally, the study
compares the sharing method with other niching techniques
Recombination and Self-Adaptation in Multi-objective Genetic Algorithms
This paper investigates the influence of recombination and self-adaptation in real-encoded Multi-Objective Genetic Algorithms (MOGAs). NSGA-II and SPEA2 are used as example to characterize the efficiency of MOGAs in relation to various recombination operators. The blend crossover, the simulated binary crossover and the breeder genetic crossover are compared for both MOGAs on multi-objective problems of the literature. Finally, a self-adaptive recombination scheme is proposed to improve the robustness of MOGAs
Sub-structural Niching in Estimation of Distribution Algorithms
We propose a sub-structural niching method that fully exploits the problem
decomposition capability of linkage-learning methods such as the estimation of
distribution algorithms and concentrate on maintaining diversity at the
sub-structural level. The proposed method consists of three key components: (1)
Problem decomposition and sub-structure identification, (2) sub-structure
fitness estimation, and (3) sub-structural niche preservation. The
sub-structural niching method is compared to restricted tournament selection
(RTS)--a niching method used in hierarchical Bayesian optimization
algorithm--with special emphasis on sustained preservation of multiple global
solutions of a class of boundedly-difficult, additively-separable multimodal
problems. The results show that sub-structural niching successfully maintains
multiple global optima over large number of generations and does so with
significantly less population than RTS. Additionally, the market share of each
of the niche is much closer to the expected level in sub-structural niching
when compared to RTS
Genetic Algorithm and its Variants: Theory and Applications
The Genetic Algorithm is a popular optimization technique which is bio-inspired and is based on the concepts of natural genetics and natural selection theories proposed by Charles Darwin. The Algorithm functions on three basic genetic operators of selection, crossover and mutation. Based on the types of these operators GA has many variants like Real coded GA, Binary coded GA, Sawtooth GA, Micro GA, Improved GA, Differential Evolution GA. This paper discusses a few of the forms of GA and applies the techniques to the problem of Function optimization and System Identification. The paper makes a comparative analysis of the advantages and disadvantages of the different types of GA. The computer simulations illustrate the results. It also makes a comparison between the GA technique and Incremental LMS algorithm for System Identification
The Novel Approach of Adaptive Twin Probability for Genetic Algorithm
The performance of GA is measured and analyzed in terms of its performance
parameters against variations in its genetic operators and associated
parameters. Since last four decades huge numbers of researchers have been
working on the performance of GA and its enhancement. This earlier research
work on analyzing the performance of GA enforces the need to further
investigate the exploration and exploitation characteristics and observe its
impact on the behavior and overall performance of GA. This paper introduces the
novel approach of adaptive twin probability associated with the advanced twin
operator that enhances the performance of GA. The design of the advanced twin
operator is extrapolated from the twin offspring birth due to single ovulation
in natural genetic systems as mentioned in the earlier works. The twin
probability of this operator is adaptively varied based on the fitness of best
individual thereby relieving the GA user from statically defining its value.
This novel approach of adaptive twin probability is experimented and tested on
the standard benchmark optimization test functions. The experimental results
show the increased accuracy in terms of the best individual and reduced
convergence time.Comment: 7 pages, International Journal of Advanced Studies in Computer
Science and Engineering (IJASCSE), Volume 2, Special Issue 2, 201
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