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Article
Publication date: 24 August 2010

Yi‐nan Guo, Mei Yang and Da‐wei Xiao

The purpose of this paper is to find a novel optimization selection method for hyper‐parameter of support vector classification (SVC), responsible for the classification…

Abstract

Purpose

The purpose of this paper is to find a novel optimization selection method for hyper‐parameter of support vector classification (SVC), responsible for the classification of datasets from the UCI machine learning database repository.

Design/methodology/approach

A novel two‐stage optimization selection method for hyper‐parameters is proposed. It makes use of explicit information derived from issues and implicit knowledge extracted from the evolution process so as to improve the performance of classifier. In the first stage, the search extent of each hyper‐parameter is determined according to the requirements of issues. In the second stage, optimal hyper‐parameters are obtained by adaptive chaotic culture algorithm in the above search extent. Adaptive chaotic cultural algorithm uses implicit knowledge extracted from the evolution process to control mutation scale of chaotic mutation operator. This algorithm can ensure the diversity of population and exploitation in the latter evolution.

Findings

The rationality of the above optimization selection method is proved by the binary classification problem. Final confirmation of this approach is the classification results compared with other methods.

Originality/value

This optimization selection method can effectively avoid premature convergence and lead to better computation stability and precision. It is not related on the structure of functions. SVC model corresponding to optimal hyper‐parameters by this method has better generalization.

Details

International Journal of Intelligent Computing and Cybernetics, vol. 3 no. 3
Type: Research Article
ISSN: 1756-378X

Keywords

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Article
Publication date: 19 June 2019

Wensheng Xiao, Qi Liu, Linchuan Zhang, Kang Li and Lei Wu

Bat algorithm (BA) is a global optimization method, but has a worse performance on engineering optimization problems. The purpose of this study is to propose a novel…

Abstract

Purpose

Bat algorithm (BA) is a global optimization method, but has a worse performance on engineering optimization problems. The purpose of this study is to propose a novel chaotic bat algorithm based on catfish effect (CE-CBA), which can effectively deal with optimization problems in real-world applications.

Design/methodology/approach

Incorporating chaos strategy and catfish effect, the proposed algorithm can not only enhance the ability for local search but also improve the ability to escape from local optima traps. On the one hand, the performance of CE-CBA has been evaluated by a set of numerical experiment based on classical benchmark functions. On the other hand, five benchmark engineering design problems have been also used to test CE-CBA.

Findings

The statistical results of the numerical experiment show the significant improvement of CE-CBA compared with the standard algorithms and improved bat algorithms. Moreover, the feasibility and effectiveness of CE-CBA in solving engineering optimization problems are demonstrated.

Originality/value

This paper proposed a novel BA with two improvement strategies including chaos strategy and catfish effect for the first time. Meanwhile, the proposed algorithm can be used to solve many real-world engineering optimization problems with several decision variables and constraints.

Details

Engineering Computations, vol. 36 no. 5
Type: Research Article
ISSN: 0264-4401

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Article
Publication date: 10 April 2007

Leandro dos Santos Coelho and Piergiorgio Alotto

This paper aims to show on a widely used benchmark problem that chaotic sequences can improve the search ability of evolution strategies (ES).

Abstract

Purpose

This paper aims to show on a widely used benchmark problem that chaotic sequences can improve the search ability of evolution strategies (ES).

Design/methodology/approach

The Lozi map is used to generate new individuals in the framework of ES algorithms. A quasi‐Newton (QN) method is also used within the iterative loop to improve the solution's quality locally.

Findings

It is shown that the combined use of chaotic sequences and QN methods can provide high‐quality solutions with small standard deviation on the selected benchmark problem.

Research limitations/implications

Although the benchmark is considered to be representative of typical electromagnetic problems, different test cases may give less satisfactory results.

Practical implications

The proposed approach appears to be an efficient general purpose optimizer for electromagnetic design problems.

Originality/value

This paper introduces the use of chaotic sequences in the area of electromagnetic design optimization.

Details

COMPEL - The international journal for computation and mathematics in electrical and electronic engineering, vol. 26 no. 2
Type: Research Article
ISSN: 0332-1649

Keywords

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Article
Publication date: 24 June 2013

Gai-Ge Wang, Amir Hossein Gandomi and Amir Hossein Alavi

To improve the performance of the krill herd (KH) algorithm, in this paper, a series of chaotic particle-swarm krill herd (CPKH) algorithms are proposed for solving…

Abstract

Purpose

To improve the performance of the krill herd (KH) algorithm, in this paper, a series of chaotic particle-swarm krill herd (CPKH) algorithms are proposed for solving optimization tasks within limited time requirements. The paper aims to discuss these issues.

Design/methodology/approach

In CPKH, chaos sequence is introduced into the KH algorithm so as to further enhance its global search ability.

Findings

This new method can accelerate the global convergence speed while preserving the strong robustness of the basic KH.

Originality/value

Here, 32 different benchmarks and a gear train design problem are applied to tune the three main movements of the krill in CPKH method. It has been demonstrated that, in most cases, CPKH with an appropriate chaotic map performs superiorly to, or at least highly competitively with, the standard KH and other population-based optimization methods.

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Article
Publication date: 14 August 2017

Ning Xian

The purpose of this paper is to propose a new algorithm chaotic pigeon-inspired optimization (CPIO), which can effectively improve the computing efficiency of the basic…

Abstract

Purpose

The purpose of this paper is to propose a new algorithm chaotic pigeon-inspired optimization (CPIO), which can effectively improve the computing efficiency of the basic Itti’s model for saliency-based detection. The CPIO algorithm and relevant applications are aimed at air surveillance for target detection.

Design/methodology/approach

To compare the improvements of the performance on Itti’s model, three bio-inspired algorithms including particle swarm optimization (PSO), brain storm optimization (BSO) and CPIO are applied to optimize the weight coefficients of each feature map in the saliency computation.

Findings

According to the experimental results in optimized Itti’s model, CPIO outperforms PSO in terms of computing efficiency and is superior to BSO in terms of searching ability. Therefore, CPIO provides the best overall properties among the three algorithms.

Practical implications

The algorithm proposed in this paper can be extensively applied for fast, accurate and multi-target detections in aerial images.

Originality/value

CPIO algorithm is originally proposed, which is very promising in solving complicated optimization problems.

Details

International Journal of Intelligent Computing and Cybernetics, vol. 10 no. 3
Type: Research Article
ISSN: 1756-378X

Keywords

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Article
Publication date: 25 November 2013

Yanming Fan and Ming Li

The purpose of this paper is to present weighted Euclidean distance for measuring whether the fitting of projective transformation matrix is more reliable in feature-based…

Abstract

Purpose

The purpose of this paper is to present weighted Euclidean distance for measuring whether the fitting of projective transformation matrix is more reliable in feature-based image stitching.

Design/methodology/approach

The hybrid model of weighted Euclidean distance criterion and intelligent chaotic genetic algorithm (CGA) is established to achieve a more accurate matrix in image stitching. Feature-based image stitching is used in this paper for it can handle non-affine situations. Scale invariant feature transform is applied to extract the key points, and the false points are excluded using random sampling consistency (RANSAC) algorithm.

Findings

This work improved GA by combination with chaos's ergodicity, so that it can be applied to search a better solution on the basis of the matrix solved by Levenberg-Marquardt. The addition of an external loop in RANSAC can help obtain more accurate matrix with large probability. Series of experimental results are presented to demonstrate the feasibility and effectiveness of the proposed approaches.

Practical implications

The modified feature-based method proposed in this paper can be easily applied to practice and can obtain a better image stitching performance with a good robustness.

Originality/value

A hybrid model of weighted Euclidean distance criterion and CGA is proposed for optimization of projective transformation matrix in image stitching. The authors introduce chaos theory into GA to modify its search strategy.

Details

International Journal of Intelligent Computing and Cybernetics, vol. 6 no. 4
Type: Research Article
ISSN: 1756-378X

Keywords

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Article
Publication date: 6 February 2020

Sajad Ahmad Rather and P. Shanthi Bala

The purpose of this paper is to investigate the performance of chaotic gravitational search algorithm (CGSA) in solving mechanical engineering design frameworks including…

Abstract

Purpose

The purpose of this paper is to investigate the performance of chaotic gravitational search algorithm (CGSA) in solving mechanical engineering design frameworks including welded beam design (WBD), compression spring design (CSD) and pressure vessel design (PVD).

Design/methodology/approach

In this study, ten chaotic maps were combined with gravitational constant to increase the exploitation power of gravitational search algorithm (GSA). Also, CGSA has been used for maintaining the adaptive capability of gravitational constant. Furthermore, chaotic maps were used for overcoming premature convergence and stagnation in local minima problems of standard GSA.

Findings

The chaotic maps have shown efficient performance for WBD and PVD problems. Further, they have depicted competitive results for CSD framework. Moreover, the experimental results indicate that CGSA shows efficient performance in terms of convergence speed, cost function minimization, design variable optimization and successful constraint handling as compared to other participating algorithms.

Research limitations/implications

The use of chaotic maps in standard GSA is a new beginning for research in GSA particularly convergence and time complexity analysis. Moreover, CGSA can be used for solving the infinite impulsive response (IIR) parameter tuning and economic load dispatch problems in electrical sciences.

Originality/value

The hybridization of chaotic maps and evolutionary algorithms for solving practical engineering problems is an emerging topic in metaheuristics. In the literature, it can be seen that researchers have used some chaotic maps such as a logistic map, Gauss map and a sinusoidal map more rigorously than other maps. However, this work uses ten different chaotic maps for engineering design optimization. In addition, non-parametric statistical test, namely, Wilcoxon rank-sum test, was carried out at 5% significance level to statistically validate the simulation results. Besides, 11 state-of-the-art metaheuristic algorithms were used for comparative analysis of the experimental results to further raise the authenticity of the experimental setup.

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Article
Publication date: 5 October 2018

Jun Guo, Jingcheng Zhong, Yibing Li, Baigang Du and Shunsheng Guo

To improve the efficiency of end-of-life product’s disassembly process, this paper aims to propose a disassembly sequence planning (DSP) method to reduce additional…

Abstract

Purpose

To improve the efficiency of end-of-life product’s disassembly process, this paper aims to propose a disassembly sequence planning (DSP) method to reduce additional efforts of removing parts when considering the changes of disassembly directions and tools.

Design/methodology/approach

The methodology has three parts. First, a disassembly hybrid graph model (DHGM) was adopted to represent disassembly operations and their precedence relations. After representing the problem as DHGM, a new integer programming model was suggested for the objective of minimizing the total disassembly time. The objective takes into account several criteria such as disassembly tools change and the change of disassembly directions. Finally, a novel hybrid approach with a chaotic mapping-based hybrid algorithm of artificial fish swarm algorithm (AFSA) and genetic algorithm (GA) was developed to find an optimal or near-optimal disassembly sequence.

Findings

Numerical experiment with case study on end-of-life product disassembly planning has been carried out to demonstrate the effectiveness of the designed criteria and the results exhibited that the developed algorithm performs better than other relevant algorithms.

Research limitations/implications

More complex case studies for DSP problems will be introduced. The performance of the CAAFG algorithm can be enhanced by improving the design of AFSA and GA by combining them with other search techniques.

Practical implications

DSP of an internal gear hydraulic pump is analyzed to investigate the accuracy and efficiency of the proposed method.

Originality/value

This paper proposes a novel CAAFG algorithm for solving DSP problems. The implemented tool generates a feasible optimal solution and the considered criteria can help the planer obtain satisfactory results.

Details

Assembly Automation, vol. 39 no. 1
Type: Research Article
ISSN: 0144-5154

Keywords

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Article
Publication date: 12 November 2020

Seyed Mohammad Javad Hosseini, Bahman Arasteh, Ayaz Isazadeh, Mehran Mohsenzadeh and Mitra Mirzarezaee

The purpose of this study is to reduce the number of mutations and, consequently, reduce the cost of mutation test. The results of related studies indicate that about 40…

Abstract

Purpose

The purpose of this study is to reduce the number of mutations and, consequently, reduce the cost of mutation test. The results of related studies indicate that about 40% of injected faults (mutants) in the source code are effect-less (equivalent). Equivalent mutants are one of the major costs of mutation testing and the identification of equivalent and effect-less mutants has been known as an undecidable problem.

Design/methodology/approach

In a program with n branch instructions (if instruction) there are 2n execution paths (test paths) that the data and codes into each of these paths can be considered as a target of mutation. Given the role and impact of data in a program, some of data and codes propagates the injected mutants more likely to the output of the program. In this study, firstly the error-propagation rate of the program data is quantified using static analysis of the program control-flow graph. Then, the most error-propagating test paths are identified by the proposed heuristic algorithm (Genetic Algorithm [GA]). Data and codes with higher error-propagation rate are only considered as the strategic locations for the mutation testing.

Findings

In order to evaluate the proposed method, an extensive series of mutation testing experiments have been conducted on a set of traditional benchmark programs using MuJava tool set. The results depict that the proposed method reduces the number of mutants about 24%. Also, in the corresponding experiments, the mutation score is increased about 5.6%. The success rate of the GA in finding the most error-propagating paths of the input programs is 99%. On average, only 7.46% of generated mutants by the proposed method are equivalent. Indeed, 92.54% of generated mutants are non-equivalent.

Originality/value

The main contribution of this study is as follows: Proposing a set of equations to measure the error-propagation rate of each data, basic-block and execution path of a program. Proposing a genetic algorithm to identify a most error-propagating path of program as locations of mutations. Developing an efficient mutation-testing framework that mutates only the strategic locations of a program identified by the proposed genetic algorithms. Reducing the time and cost of mutation testing by reducing the equivalent mutants.

Details

Data Technologies and Applications, vol. 55 no. 1
Type: Research Article
ISSN: 2514-9288

Keywords

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Article
Publication date: 10 July 2009

Lucas de S. Batista, Jaime A. Ramírez and Frederico G. Guimarães

The purpose of this paper is to present a new multi‐objective clonal selection algorithm (MCSA) for the solution of electromagnetic optimization problems.

Abstract

Purpose

The purpose of this paper is to present a new multi‐objective clonal selection algorithm (MCSA) for the solution of electromagnetic optimization problems.

Design/methodology/approach

The method performs the somatic hypermutation step using different probability distributions, balancing the local search in the algorithm. Furthermore, it includes a receptor editing operator that implicitly realizes a dynamic search over the landscape.

Findings

In order to illustrate the efficiency of MCSA, its performance is compared with the nondominated sorting genetic algorithm II (NSGA‐II) in some analytical problems and in the well‐known TEAM benchmark Problem 22. Three performance evaluation techniques are used in the comparison, and the effect of each operator of the MCSA in its accomplishment is estimated.

Research limitations/implications

In the analytical problems, the MCSA enhanced both the extension and uniformity in its solutions, providing better Pareto‐optimal sets than the NSGA‐II. In the Problem 22, the MCSA also outperformed the NSGA‐II. The MCSA was not dominated by the NSGA‐II in the three variables case and clearly presented a better convergence speed in the eight variables problem.

Practical implications

This paper could be useful for researchers who deal with multi‐objective optimization problems involving high‐computational cost.

Originality/value

The new operators incorporated in the MCSA improved both the extension, uniformity and the convergence speed of the solutions, in terms of the number of function evaluations, representing a robust tool for real‐world optimization problems.

Details

COMPEL - The international journal for computation and mathematics in electrical and electronic engineering, vol. 28 no. 4
Type: Research Article
ISSN: 0332-1649

Keywords

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