Diploidy in evolutionary algorithms for dynamic optimization problems

2015 ◽  
Vol 8 (4) ◽  
pp. 312-329 ◽  
Author(s):  
Boris Shabash ◽  
Kay C. Wiese
Keyword(s):  
Evolutionary Algorithms ◽  
Dynamic Optimization ◽  
Genetic Information ◽  
Optimization Problems ◽  
Fitness Function ◽  
Search Space ◽  
Global Optimum ◽  
Dynamic Optimization Problems ◽  
Content Type ◽  
Optimization Function

Purpose – In this work, the authors show the performance of the proposed diploid scheme (a representation where each individual contains two genotypes) with respect to two dynamic optimization problems, while addressing drawbacks the authors have identified in previous works which compare diploid evolutionary algorithms (EAs) to standard EAs. The paper aims to discuss this issue. Design/methodology/approach – In the proposed diploid representation of EA, each individual possesses two copies of the genotype. In order to convert this pair of genotypes to a single phenotype, each genotype is individually evaluated in relation to the fitness function and the best genotype is presented as the phenotype. In order to provide a fair and objective comparison, the authors make sure to compare populations which contain the same amount of genetic information, where the only difference is the arrangement and interpretation of the information. The two representations are compared using two shifting fitness functions which change at regular intervals to displace the global optimum to a new position. Findings – For small fitness landscapes the haploid (standard) and diploid algorithms perform comparably and are able to find the global optimum very quickly. However, as the search space increases, rediscovering the global optimum becomes more difficult and the diploid algorithm outperforms the haploid algorithm with respect to how fast it relocates the new optimum. Since both algorithms use the same amount of genetic information, it is only fair to conclude it is the unique arrangement of the diploid algorithm that allows it to explore the search space better. Originality/value – The diploid representation presented here is novel in that instead of adopting a dominance scheme for each allele (value) in the vector of values that is the genotype, dominance is adopted across the entire genotype in relation to its homologue. As a result, this representation can be extended across any alphabet, for any optimization function.

Genetic Algorithms with Immigrants Scheme for Dynamic Optimization Problems

2013 ◽  
Vol 333-335 ◽  
pp. 1379-1383
Author(s):  
Yan Wu ◽  
Xiao Xiong Liu
Keyword(s):  
Genetic Algorithms ◽  
Dynamic Optimization ◽  
Optimization Problems ◽  
Main Idea ◽  
Optimal Solution ◽  
Search Space ◽  
Dynamic Environments ◽  
Dynamic Optimization Problems ◽  
New Space ◽  
Over Time

In dynamic environments, it is difficult to track a changing optimal solution over time. Over the years, many approaches have been proposed to solve the problem with genetic algorithms. In this paper a new space-based immigrant scheme for genetic algorithms is proposed to solve dynamic optimization problems. In this scheme, the search space is divided into two subspaces using the elite of the previous generation and the range of variables. Then the immigrants are generated from both the subspaces and inserted into current population. The main idea of the approach is to increase the diversity more evenly and dispersed. Finally an experimental study on dynamic sphere function was carried out to compare the performance of several genetic algorithms. The experimental results show that the proposed algorithm is effective for the function with moving optimum and can adapt the dynamic environments rapidly.

Reverse Search Particle Swarm Optimization to Deal with Dynamic Optimization Problems

2014 ◽  
Vol 571-572 ◽  
pp. 245-251
Author(s):  
Li Chen ◽  
Wei Jiang Wang ◽  
Lei Yao
Keyword(s):  
Particle Swarm Optimization ◽  
Dynamic Optimization ◽  
Optimization Problems ◽  
Fitness Function ◽  
Dynamic Environment ◽  
Particle Swarm ◽  
Dynamic Optimization Problems ◽  
Swarm Optimization ◽  
Promising Area ◽  
Coarse Search

Multiswarm approaches are used in many literatures to deal with dynamic optimization problems (DOPs). Each swarm tries to find promising areas where usually peaks lie and many good results have been obtained. However, steep peaks are difficult to be found with multiswarm approaches , which hinders the performance of the algorithm to be improved furtherly. Aiming at the bottleneck, the paper introduces the idea of sequential niche technique to traditional multiswarm approach and thus proposes a novel algorithm called reverse space search multiswarm particle swarm optimization (RSPSO) for DOPs. RSPSO uses the information of the peaks found by coarse search of traditional multiswarm approach to modify the original fitness function. A newly generated subswarm - reverse search subswarm evolves with the modified fitness function, at the same time, other subswarms using traditional mltiswarm approach still evolve. Two kinds of subswarm evolve in cooperation. Reverse search subswarm tends to find much steeper peak and so more promising area where peaks lie is explored. Elaborated experiments on MPB show the introduction of reverse search enhances the ability of finding peaks , the performance of RSPSO significantly outperforms traditional multiswarm approaches and it has better robustness to adapt to dynamic environment with wider-range change severity.

scholarly journals An Adaptive Multi-Swarm Optimizer for Dynamic Optimization Problems

2014 ◽  
Vol 22 (4) ◽  
pp. 559-594 ◽  
Author(s):  
Changhe Li ◽  
Shengxiang Yang ◽  
Ming Yang
Keyword(s):  
Experimental Study ◽  
Dynamic Optimization ◽  
Optimization Problems ◽  
Adaptive Algorithms ◽  
Dynamic Environments ◽  
Global Optimum ◽  
Population Diversity ◽  
Dynamic Optimization Problems ◽  
Research Areas ◽  
Moving Peaks

The multipopulation method has been widely used to solve dynamic optimization problems (DOPs) with the aim of maintaining multiple populations on different peaks to locate and track multiple changing optima simultaneously. However, to make this approach effective for solving DOPs, two challenging issues need to be addressed. They are how to adapt the number of populations to changes and how to adaptively maintain the population diversity in a situation where changes are complicated or hard to detect or predict. Tracking the changing global optimum in dynamic environments is difficult because we cannot know when and where changes occur and what the characteristics of changes would be. Therefore, it is necessary to take these challenging issues into account in designing such adaptive algorithms. To address the issues when multipopulation methods are applied for solving DOPs, this paper proposes an adaptive multi-swarm algorithm, where the populations are enabled to be adaptive in dynamic environments without change detection. An experimental study is conducted based on the moving peaks problem to investigate the behavior of the proposed method. The performance of the proposed algorithm is also compared with a set of algorithms that are based on multipopulation methods from different research areas in the literature of evolutionary computation.

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