Search results

1 – 10 of over 4000
Click here to view access options
Article
Publication date: 11 June 2018

Ahmad Mozaffari

In recent decades, development of effective methods for optimizing a set of conflicted objective functions has been absorbing an increasing interest from researchers. This…

Abstract

Purpose

In recent decades, development of effective methods for optimizing a set of conflicted objective functions has been absorbing an increasing interest from researchers. This refers to the essence of real-life engineering systems and complex natural mechanisms which are generally multi-modal, non-convex and multi-criterion. Until now, several deterministic and stochastic methods have been proposed to cope with such complex systems. Advanced soft computational methods such as evolutionary games (cooperative and non-cooperative), Pareto-based techniques, fuzzy evolutionary methods, cooperative bio-inspired algorithms and neuro-evolutionary systems have effectively come to the aid of researchers to build up efficient paradigms with application to vector optimization. The paper aims to discuss this issue.

Design/methodology/approach

A novel hybrid algorithm called synchronous self-learning Pareto strategy (SSLPS) is presented for the sake of vector optimization. The method is the ensemble of evolutionary algorithms (EA), swarm intelligence (SI), adaptive version of self-organizing map (CSOM) and a data shuffling mechanism. EA are powerful numerical optimization algorithms capable of finding a global extreme point over a wide exploration domain. SI techniques (the swarm of bees in our case) can improve both intensification and robustness of exploration. CSOM network is an unsupervised learning methodology which learns the characteristics of non-dominated solutions and, thus, enhances the quality of the Pareto front.

Findings

To prove the effectiveness of the proposed method, the authors engage a set of well-known benchmark functions and some well-known rival optimization methods. Additionally, SSLPS is employed for optimal design of shape memory alloy actuator as a nonlinear multi-modal real-world engineering problem. The experiments show the acceptable potential of SSLPS for handling both numerical and engineering multi-objective problems.

Originality/value

To the author’s best knowledge, the proposed algorithm is among the rare multi-objective methods which fosters the use of automated unsupervised learning for increasing the intensity of Pareto front (while preserving the diversity). Also, the research evaluates the power of hybridization of SI and EA for efficient search.

Details

International Journal of Intelligent Computing and Cybernetics, vol. 11 no. 2
Type: Research Article
ISSN: 1756-378X

Keywords

Click here to view access options
Article
Publication date: 22 November 2011

Jingjing Ma, Maoguo Gong and Licheng Jiao

The purpose of this paper is to present an evolutionary clustering algorithm based on mixed measure for complex distributed data.

Abstract

Purpose

The purpose of this paper is to present an evolutionary clustering algorithm based on mixed measure for complex distributed data.

Design/methodology/approach

In this method, the data are first partitioned into some spherical distributed sub‐clusters by using the Euclidean distance as the similarity measurement, and each clustering center represents all the members of corresponding cluster. Then, the clustering centers obtained in the first phase are clustered by using a novel manifold distance as the similarity measurement. The two clustering processes in this method are both based on evolutionary algorithm.

Findings

Theoretical analysis and experimental results on seven artificial data sets and seven UCI data sets with different structures show that the novel algorithm has the ability to identify clusters efficiently with no matter simple or complex, convex or non‐convex distribution. When compared with the genetic algorithm‐based clustering and the K‐means algorithm, the proposed algorithm outperformed the compared algorithms on most of the test data sets.

Originality/value

The method presented in this paper represents a new approach to solving clustering problems of complex distributed data. The novel method applies the idea “coarse clustering, fine clustering”, which executes coarse clustering by Euclidean distance and fine clustering by manifold distance as similarity measurements, respectively. The proposed clustering algorithm is shown to be effective in solving data clustering problems with different distribution.

Details

International Journal of Intelligent Computing and Cybernetics, vol. 4 no. 4
Type: Research Article
ISSN: 1756-378X

Keywords

Click here to view access options
Article
Publication date: 22 February 2013

Zhi‐Hua Hu, Xiao‐Kun Yu and Zhao‐Han Sheng

The purpose of this paper is to study the problem of clothing uniform assignment (CUA) and propose an immune co‐evolutionary algorithm to search optimal assignments of…

Abstract

Purpose

The purpose of this paper is to study the problem of clothing uniform assignment (CUA) and propose an immune co‐evolutionary algorithm to search optimal assignments of uniform garments to employees.

Design/methodology/approach

Multi‐size fitting measures are proposed based on multi‐attribute decision making. An immune co‐evolutionary algorithm incorporating immune inspired mechanisms is proposed to search optimal assignments.

Findings

The experimental results show promising performance. The model and the algorithm are aiming at a valuable problem and can be incorporated into the information systems for large‐scale industrial companies.

Originality/value

Uniform assignment problem is modeled with garment size fitting constraints. Multi‐size fitting measures are proposed based on multi‐attribute decision making and an immune co‐evolutionary algorithm is proposed to search optimal assignments.

Click here to view access options
Article
Publication date: 4 November 2014

Ahmad Mozaffari, Nasser Lashgarian Azad and Alireza Fathi

The purpose of this paper is to demonstrate the applicability of swarm and evolutionary techniques for regularized machine learning. Generally, by defining a proper…

Abstract

Purpose

The purpose of this paper is to demonstrate the applicability of swarm and evolutionary techniques for regularized machine learning. Generally, by defining a proper penalty function, regularization laws are embedded into the structure of common least square solutions to increase the numerical stability, sparsity, accuracy and robustness of regression weights. Several regularization techniques have been proposed so far which have their own advantages and disadvantages. Several efforts have been made to find fast and accurate deterministic solvers to handle those regularization techniques. However, the proposed numerical and deterministic approaches need certain knowledge of mathematical programming, and also do not guarantee the global optimality of the obtained solution. In this research, the authors propose the use of constraint swarm and evolutionary techniques to cope with demanding requirements of regularized extreme learning machine (ELM).

Design/methodology/approach

To implement the required tools for comparative numerical study, three steps are taken. The considered algorithms contain both classical and swarm and evolutionary approaches. For the classical regularization techniques, Lasso regularization, Tikhonov regularization, cascade Lasso-Tikhonov regularization, and elastic net are considered. For swarm and evolutionary-based regularization, an efficient constraint handling technique known as self-adaptive penalty function constraint handling is considered, and its algorithmic structure is modified so that it can efficiently perform the regularized learning. Several well-known metaheuristics are considered to check the generalization capability of the proposed scheme. To test the efficacy of the proposed constraint evolutionary-based regularization technique, a wide range of regression problems are used. Besides, the proposed framework is applied to a real-life identification problem, i.e. identifying the dominant factors affecting the hydrocarbon emissions of an automotive engine, for further assurance on the performance of the proposed scheme.

Findings

Through extensive numerical study, it is observed that the proposed scheme can be easily used for regularized machine learning. It is indicated that by defining a proper objective function and considering an appropriate penalty function, near global optimum values of regressors can be easily obtained. The results attest the high potentials of swarm and evolutionary techniques for fast, accurate and robust regularized machine learning.

Originality/value

The originality of the research paper lies behind the use of a novel constraint metaheuristic computing scheme which can be used for effective regularized optimally pruned extreme learning machine (OP-ELM). The self-adaption of the proposed method alleviates the user from the knowledge of the underlying system, and also increases the degree of the automation of OP-ELM. Besides, by using different types of metaheuristics, it is demonstrated that the proposed methodology is a general flexible scheme, and can be combined with different types of swarm and evolutionary-based optimization techniques to form a regularized machine learning approach.

Details

International Journal of Intelligent Computing and Cybernetics, vol. 7 no. 4
Type: Research Article
ISSN: 1756-378X

Keywords

Click here to view access options
Article
Publication date: 16 August 2021

Babitha Thangamalar J. and Abudhahir A.

This study aims to propose optimised function-based evolutionary algorithms in this research to effectively replace the traditional electronic circuitry used in…

Abstract

Purpose

This study aims to propose optimised function-based evolutionary algorithms in this research to effectively replace the traditional electronic circuitry used in linearising constant temperature anemometer (CTA) and Microbridge mass flow sensor AWM 5000.

Design/methodology/approach

The proposed linearisation technique effectively uses the ratiometric function for the linearisation of CTA and Microbridge mass flow sensor AWM 5000. In addition, the well-known transfer relation, namely, the King’s Law is used for the linearisation of CTA and successfully implemented using LabVIEW 7.1.

Findings

Investigational results unveil that the proposed evolutionary optimised linearisation technique performs better in linearisation of both CTA and Mass flow sensors, and hence finds applications for computer-based flow measurement/control systems.

Originality/value

The evolutionary optimisation algorithms such as the real-coded genetic algorithm, particle swarm optimisation algorithm, differential evolution algorithm and covariance matrix adopted evolutionary strategy algorithm are used to determine the optimal values of the parameters present in the proposed ratiometric function. The performance measures, namely, the full-scale error and mean square error are used to analyse the overall performance of the proposed approach is compared to a state of art techniques available in the literature.

Details

Circuit World, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0305-6120

Keywords

Click here to view access options
Article
Publication date: 24 April 2020

Faqihza Mukhlish, John Page and Michael Bain

This paper aims to propose a novel epigenetic learning (EpiLearn) algorithm, which is designed specifically for a decentralised multi-agent system such as swarm robotics.

Abstract

Purpose

This paper aims to propose a novel epigenetic learning (EpiLearn) algorithm, which is designed specifically for a decentralised multi-agent system such as swarm robotics.

Design/methodology/approach

First, this paper begins with overview of swarm robotics and the challenges in designing swarm behaviour automatically. This should indicate the direction of improvements required to enhance an automatic swarm design. Second, the evolutionary learning (EpiLearn) algorithm for a swarm system using an epigenetic layer is formulated and discussed. The algorithm is then tested through various test functions to investigate its performance. Finally, the results are discussed along with possible future research directions.

Findings

Through various test functions, the algorithm can solve non-local and many local minima problems. This article also shows that by using a reward system, the algorithm can handle the deceptive problem which often occurs in dynamic problems. Moreover, utilization of rewards from the environment in the form of a methylation process on the epigenetic layer improves the performance of traditional evolutionary algorithms applied to automatic swarm design. Finally, this article shows that a regeneration process that embeds an epigenetic layer in the inheritance process performs better than a traditional crossover operator in a swarm system.

Originality/value

This paper proposes a novel method for automatic swarm design by taking into account the importance of multi-agent settings and environmental characteristics surrounding the swarm. The novel evolutionary learning (EpiLearn) algorithm using an epigenetic layer gives the swarm the ability to perform co-evolution and co-learning.

Details

International Journal of Intelligent Unmanned Systems, vol. 8 no. 3
Type: Research Article
ISSN: 2049-6427

Keywords

Click here to view access options
Article
Publication date: 23 August 2018

Luis Martí, Eduardo Segredo, Nayat Sánchez-Pi and Emma Hart

One of the main components of multi-objective, and therefore, many-objective evolutionary algorithms, is the selection mechanism. It is responsible for performing two main…

Abstract

Purpose

One of the main components of multi-objective, and therefore, many-objective evolutionary algorithms, is the selection mechanism. It is responsible for performing two main tasks simultaneously. First, it has to promote convergence by selecting solutions which are as close as possible to the Pareto optimal set. And second, it has to promote diversity in the solution set provided. In the current work, an exhaustive study that involves the comparison of several selection mechanisms with different features is performed. Particularly, Pareto-based and indicator-based selection schemes, which belong to well-known multi-objective optimisers, are considered. The paper aims to discuss these issues.

Design/methodology/approach

Each of those mechanisms is incorporated into a common multi-objective evolutionary algorithm framework. The main goal of the study is to measure the diversity preserved by each of those selection methods when addressing many-objective optimisation problems. The Walking Fish Group test suite, a set of optimisation problems with a scalable number of objective functions, is taken into account to perform the experimental evaluation.

Findings

The computational results highlight that the the reference-point-based selection scheme of the Non-dominated Sorting Genetic Algorithm III and a modified version of the Non-dominated Sorting Genetic Algorithm II, where the crowding distance is replaced by the Euclidean distance, are able to provide the best performance, not only in terms of diversity preservation, but also in terms of convergence.

Originality/value

The performance provided by the use of the Euclidean distance as part of the selection scheme indicates this is a promising line of research and, to the best of the knowledge, it has not been investigated yet.

Click here to view access options
Article
Publication date: 9 November 2015

Boris Shabash and Kay C. Wiese

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…

Abstract

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.

Details

International Journal of Intelligent Computing and Cybernetics, vol. 8 no. 4
Type: Research Article
ISSN: 1756-378X

Keywords

Click here to view access options
Article
Publication date: 2 March 2015

Zhi-Hua Hu, Chen Wei and Xiao-Kun Yu

The purpose of this paper is to study the problem of a routing problem with uncertain try-on service time (VRPUS) for apparel distribution, and to devise solution…

Abstract

Purpose

The purpose of this paper is to study the problem of a routing problem with uncertain try-on service time (VRPUS) for apparel distribution, and to devise solution strategies coping with the uncertainty by an evolutionary algorithm. VRPUS belongs to the category of practical routing models integrated with uncertain service times. However, in the background of apparel distribution, it has distinct features. The try-on service will improve the customer satisfaction by providing experiences to customers; the return cost is saved; the customer loyalty is improved for experiencing face-to-face try-on services. However, the uncertainty of try-on service time makes the apparel distribution process uncertain and incurs additional risk management cost, such that the logistics companies should optimally make decisions on the choice of the service and the service processes.

Design/methodology/approach

This paper devised a mixed-integer programming (MIP) model for the base vehicle routing problem (VRP) and then it is extended to support the solution strategies for uncertain try-on times. A try-on time estimation parameter and a time reservation parameter are used to cope with the uncertain try-on time, and the try-on rejection strategy is applied when the uncertain try-on time is realized at customer and no surplus time can be used for try-on service besides distributing to remainder customers. Due to the computational complexity of VRPUS, an evolutionary algorithm is designed for solving it. These parameters and strategy options are designed for the operational decisions by logistics companies. Finally, a decision support system (DSS) is designed.

Findings

Five experimental scenarios are performed to reveal the impacts of parameters and solution strategies coping with uncertain try-on time on the distribution cost, return cost, and the try-on service failure. The tuning methods are designed to assist the decisions by logistics companies.

Originality/value

A new routing problem is addressed for apparel distribution in fashion industry especially in the context of booming apparel e-commerce, which is a VRP with uncertain try-on service time for apparel distribution; three strategies are developed to cope with the try-on time uncertainty. The proposed method is also a theoretical base for designing a practical DSS for logistics companies to provide try-on service to customers.

Details

International Journal of Clothing Science and Technology, vol. 27 no. 1
Type: Research Article
ISSN: 0955-6222

Keywords

Click here to view access options
Article
Publication date: 1 May 2002

Kwong‐Sak Leung, Jian‐Yong Sun and Zong‐Ben Xu

In this paper, a set of safe adaptive genetic algorithms (sGAs) is proposed based on the Splicing/Decomposable encoding scheme and the efficient speed‐up strategies…

Abstract

In this paper, a set of safe adaptive genetic algorithms (sGAs) is proposed based on the Splicing/Decomposable encoding scheme and the efficient speed‐up strategies developed by Xu et al.. The proposed algorithms implement the self‐adaptation of the problem representation, selection and recombination operators at the levels of population, individual and component which commendably balance the conflicts between “reliability” and “efficiency”, as well as “exploitation” and “exploration” existed in the evolutionary algorithms. It is shown that the algorithms converge to the optimum solution in probability one. The proposed sGAs are experimentally compared with the classical genetic algorithm (CGA), non‐uniform genetic algorithm (nGA) proposed by Michalewicz, forking genetic algorithm (FGA) proposed by Tsutsui et al. and the classical evolution programming (CEP). The experiments indicate that the new algorithms perform much more efficiently than CGA and FGA do, comparable with the real‐coded GAs — nGA and CEP. All the algorithms are further evaluated through an application to a difficult real‐life application problem: the inverse problem of fractal encoding related to fractal image compression technique. The results for the sGA is better than those of CGA and FGA, and has the same, sometimes better performance compared to those of nGA and CEP.

Details

Engineering Computations, vol. 19 no. 3
Type: Research Article
ISSN: 0264-4401

Keywords

1 – 10 of over 4000