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Article
Publication date: 1 March 2013

Zhang Ping, Wei Ping, Fei Chun and Yu Hong‐yang

This paper proposes a hybrid biogeography‐based optimization (BBO) with simplex method (SM) algorithm (HSMBBO).

Abstract

Purpose

This paper proposes a hybrid biogeography‐based optimization (BBO) with simplex method (SM) algorithm (HSMBBO).

Design/methodology/approach

BBO is a new intelligent optimization algorithm. The global optimization ability of BBO is better than that of genetic algorithm (GA) and particle swarm optimization (PSO), but BBO also easily falls into local minimum. To improve BBO, HSMBBO combines BBO and SM, which makes full use of the high local search ability of SM. In HSMBBO, BBO is used firstly to obtain the current global solution. Then SM is searched to acquire the optimum solution based on that global solution. Due to the searching of SM, the search range is expanded and the speed of convergence is faster. Meanwhile, HSMBBO is applied to motion estimation of video coding.

Findings

In total, six benchmark functions with multimodal and high dimension are tested. Simulation results show that HSMBBO outperforms GA, PSO and BBO in converging speed and global search ability. Meanwhile, the application results show that HSMBBO performs better than GA, PSO and BBO in terms of both searching precision and time‐consumption.

Originality/value

The proposed algorithm improves the BBO algorithm and provides a new approach for motion estimation of video coding.

Details

COMPEL - The international journal for computation and mathematics in electrical and electronic engineering, vol. 32 no. 2
Type: Research Article
ISSN: 0332-1649

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Article
Publication date: 9 March 2015

Jehad Ababneh

– The purpose of this paper is to propose an algorithm that combines the particle swarm optimization (PSO) with the biogeography-based optimization (BBO) algorithm.

Abstract

Purpose

The purpose of this paper is to propose an algorithm that combines the particle swarm optimization (PSO) with the biogeography-based optimization (BBO) algorithm.

Design/methodology/approach

The BBO and the PSO algorithms are jointly used in to order to combine the advantages of both algorithms. The efficiency of the proposed algorithm is tested using some selected standard benchmark functions. The performance of the proposed algorithm is compared with that of the differential evolutionary (DE), genetic algorithm (GA), PSO, BBO, blended BBO and hybrid BBO-DE algorithms.

Findings

Experimental results indicate that the proposed algorithm outperforms the BBO, PSO, DE, GA, and the blended BBO algorithms and has comparable performance to that of the hybrid BBO-DE algorithm. However, the proposed algorithm is simpler than the BBO-DE algorithm since the PSO does not have complex operations such as mutation and crossover used in the DE algorithm.

Originality/value

The proposed algorithm is a generic algorithm that can be used to efficiently solve optimization problems similar to that solved using other popular evolutionary algorithms but with better performance.

Details

International Journal of Intelligent Computing and Cybernetics, vol. 8 no. 1
Type: Research Article
ISSN: 1756-378X

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Article
Publication date: 20 April 2020

Nurcan Sarikaya Basturk and Abdurrahman Sahinkaya

The purpose of this paper is to present a detailed performance comparison of recent and state-of-the-art population-based optimization algorithms for the air traffic…

Abstract

Purpose

The purpose of this paper is to present a detailed performance comparison of recent and state-of-the-art population-based optimization algorithms for the air traffic control problem.

Design/methodology/approach

Landing sequence and corresponding landing times for the aircrafts were determined by using population-based optimization algorithms such as artificial bee colony, particle swarm, differential evolution, biogeography-based optimization, simulated annealing, firefly and teaching–learning-based optimization. To obtain a fair comparison, all simulations were repeated 30 times for each of the seven algorithms, two different problems and two different population sizes, and many different criteria were used.

Findings

Compared to conventional methods that depend on a single solution at the same time, population-based algorithms have simultaneously produced many alternate possible solutions that can be used recursively to achieve better results.

Research limitations/implications

In some cases, it may take slightly longer to obtain the optimum landing sequence and times compared to the methods that give a direct result; however, the processing times can be reduced using powerful computers or GPU computations.

Practical implications

The simulation results showed that using population-based optimization algorithms were useful to obtain optimal landing sequence and corresponding landing times. Thus, the proposed air traffic control method can also be used effectively in real airport applications.

Social implications

By using population-based algorithms, air traffic control can be performed more effectively. In this way, there will be more efficient planning of passengers’ travel schedules and efficient airport operations.

Originality/value

The study compares the performances of recent and state-of-the-art optimization algorithms in terms of effective air traffic control and provides a useful approach.

Details

Aircraft Engineering and Aerospace Technology, vol. 92 no. 6
Type: Research Article
ISSN: 1748-8842

Keywords

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Article
Publication date: 5 May 2015

Weiren Zhu and Haibin Duan

The purpose of this paper is to propose a novel Unmanned Combat Air Vehicle (UCAV) flight controller parameters identification method, which is based on predator-prey…

Abstract

Purpose

The purpose of this paper is to propose a novel Unmanned Combat Air Vehicle (UCAV) flight controller parameters identification method, which is based on predator-prey Biogeography-Based Optimization (PPBBO) algorithm, with the objective of optimizing the whole UCAV system design process.

Design/methodology/approach

The hybrid model of predator-prey theory and biogeography-based optimization (BBO) algorithm is established for parameters identification of UCAV. This proposed method identifies controller parameters and reduces the computational complexity.

Findings

The basic BBO is improved by modifying the search strategy and adding some limits, so that it can be better applied to the parameters identification problem. Comparative experimental results demonstrated the feasibility and effectiveness of the proposed method: it can guarantee finding the optimal controller parameters, with the rapid convergence.

Practical implications

The proposed PPBBO algorithm can be easily applied to practice and can help the design of the UCAV flight control system, which will considerably increase the autonomy of the UCAV.

Originality/value

A hybrid model of predator-prey theory and BBO algorithm is proposed for parameters identification of UCAV, and a PPBBO-based software platform for UCAV controller design is also developed.

Details

Aircraft Engineering and Aerospace Technology: An International Journal, vol. 87 no. 3
Type: Research Article
ISSN: 0002-2667

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Article
Publication date: 13 June 2016

Qingzheng Xu, Na Wang and Lei Wang

The purpose of this paper is to examine and compare the entire impact of various execution skills of oppositional biogeography-based optimization using the current optimum…

Abstract

Purpose

The purpose of this paper is to examine and compare the entire impact of various execution skills of oppositional biogeography-based optimization using the current optimum (COOBBO) algorithm.

Design/methodology/approach

The improvement measures tested in this paper include different initialization approaches, crossover approaches, local optimization approaches, and greedy approaches. Eight well-known traveling salesman problems (TSP) are employed for performance verification. Four comparison criteria are recoded and compared to analyze the contribution of each modified method.

Findings

Experiment results illustrate that the combination model of “25 nearest-neighbor algorithm initialization+inver-over crossover+2-opt+all greedy” may be the best choice of all when considering both the overall algorithm performance and computation overhead.

Originality/value

When solving TSP with varying scales, these modified methods can enhance the performance and efficiency of COOBBO algorithm in different degrees. And an appropriate combination model may make the fullest possible contribution.

Details

International Journal of Intelligent Computing and Cybernetics, vol. 9 no. 2
Type: Research Article
ISSN: 1756-378X

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Article
Publication date: 23 August 2011

Hongwei Mo and Lifang Xu

Biogeography‐based optimization algorithm is a new kind of optimization algorithm based on biogeography. It is designed based on the migration strategy of animals to solve…

Abstract

Purpose

Biogeography‐based optimization algorithm is a new kind of optimization algorithm based on biogeography. It is designed based on the migration strategy of animals to solve the problem of optimization. The purpose of this paper is to present a new algorithm – biogeography migration algorithm for traveling salesman problem (TSPBMA). A new special migration operator is designed for producing new solutions.

Design/methodology/approach

The paper gives the definition of TSP and models of TSPBMA; introduces the algorithm of TSPBMA in detail and gives the proof of convergence in theory; provides simulation results of TSPBMA compared with other optimization algorithms for TSP and presents some concluding remarks and suggestions for further work.

Findings

The TSPBMA is tested on some classical TSP problems. The comparison results with the other nature‐inspired optimization algorithms show that TSPBMA is useful for TSP combination optimization. Especially, the designed migration operator is very effective for TSP solving. Although the proposed TSPBMA is not better than ant colony algorithm in the respect of convergence speed and accuracy, it provides a new way for this kind of problem.

Originality/value

The migration operator is a new strategy for solving TSPs. It has never been used by any other evolutionary algorithm or swarm intelligence before TSPBMA.

Details

International Journal of Intelligent Computing and Cybernetics, vol. 4 no. 3
Type: Research Article
ISSN: 1756-378X

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Article
Publication date: 22 February 2019

Maurizio Faccio, Mojtaba Nedaei and Francesco Pilati

The current study aims to propose a new analytical approach by considering energy consumption (EC), maximum tardiness and completion time as the primary objective…

Abstract

Purpose

The current study aims to propose a new analytical approach by considering energy consumption (EC), maximum tardiness and completion time as the primary objective functions to assess the performance of parallel, non-bottleneck and multitasking machines operating in dynamic job shops.

Design/methodology/approach

An analytical and iterative method is presented to optimize a novel dynamic job shop under technical constraints. The machine’s performance is analyzed by considering the setup energy. An optimization model from initial processing until scheduling and planning is proposed, and data sets consisting of design parameters are fed into the model.

Findings

Significant variations of EC and tardiness are observed. The minimum EC was calculated to be 141.5 hp.s when the defined decision variables were constantly increasing. Analysis of the optimum completion time has shown that among all studied methods, first come first served (FCFS), earliest due date (EDD) and shortest processing time (SPT) have resulted in the least completion time with a value of 20 s.

Originality/value

Considerable amount of energy can be dissipated when parallel, non-bottleneck and multitasking machines operate in lower-power modes. Additionally, in a dynamic job shop, adjusting the trend and arrangement of decision variables plays a crucial role in enhancing the system’s reliability. Such issues have never caught the attention of scientists for addressing the aforementioned problems. Therefore, with these underlying goals, this paper presents a new approach for evaluating and optimizing the system’s performance, considering different objective functions and technical constraints.

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Article
Publication date: 7 February 2020

Haiyan Zhuang and Babak Esmaeilpour Ghouchani

Virtual machines (VMs) are suggested by the providers of cloud services as the services for the users over the internet. The consolidation of VM is the tactic of the…

Abstract

Purpose

Virtual machines (VMs) are suggested by the providers of cloud services as the services for the users over the internet. The consolidation of VM is the tactic of the competent and smart utilization of resources from cloud data centers. Placement of a VM is one of the significant issues in cloud computing (CC). Physical machines in a cloud environment are aware of the way of the VM placement (VMP) as the mapping VMs. The basic target of placement of VM issue is to reduce the physical machines' items that are running or the hosts in cloud data centers. The VMP methods have an important role in the CC. However, there is no systematic and complete way to discuss and analyze the algorithms. The purpose of this paper is to present a systematic survey of VMP techniques. Also, the benefits and weaknesses connected with selected VMP techniques have been debated, and the significant issues of these techniques are addressed to develop the more efficient VMP technique for the future.

Design/methodology/approach

Because of the importance of VMP in the cloud environments, in this paper, the articles and important mechanisms in this domain have been investigated systematically. The VMP mechanisms have been categorized into two major groups, including static and dynamic mechanisms.

Findings

The results have indicated that an appropriate VMP has the capacity to decrease the resource consumption rate, energy consumption and carbon emission rate. VMP approaches in computing environment still need improvements in terms of reducing related overhead, consolidation of the cloud environment to become an extremely on-demand mechanism, balancing the load between physical machines, power consumption and refining performance.

Research limitations/implications

This study aimed to be comprehensive, but there were some limitations. Some perfect work may be eliminated because of applying some filters to choose the original articles. Surveying all the papers on the topic of VMP is impossible, too. Nevertheless, the authors are trying to present a complete survey over the VMP.

Practical implications

The consequences of this research will be valuable for academicians, and it can provide good ideas for future research in this domain. By providing comparative information and analyzing the contemporary developments in this area, this research will directly support academics and working professionals for better knowing the growth in the VMP area.

Originality/value

The gathered information in this paper helps to inform the researchers with the state of the art in the VMP area. Totally, the VMP's principal intention, current challenges, open issues, strategies and mechanisms in cloud systems are summarized by explaining the answers.

Details

Kybernetes, vol. 50 no. 2
Type: Research Article
ISSN: 0368-492X

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Article
Publication date: 30 June 2020

Sajad Ahmad Rather and P. Shanthi Bala

In this paper, a newly proposed hybridization algorithm namely constriction coefficient-based particle swarm optimization and gravitational search algorithm (CPSOGSA) has…

Abstract

Purpose

In this paper, a newly proposed hybridization algorithm namely constriction coefficient-based particle swarm optimization and gravitational search algorithm (CPSOGSA) has been employed for training MLP to overcome sensitivity to initialization, premature convergence, and stagnation in local optima problems of MLP.

Design/methodology/approach

In this study, the exploration of the search space is carried out by gravitational search algorithm (GSA) and optimization of candidate solutions, i.e. exploitation is performed by particle swarm optimization (PSO). For training the multi-layer perceptron (MLP), CPSOGSA uses sigmoid fitness function for finding the proper combination of connection weights and neural biases to minimize the error. Secondly, a matrix encoding strategy is utilized for providing one to one correspondence between weights and biases of MLP and agents of CPSOGSA.

Findings

The experimental findings convey that CPSOGSA is a better MLP trainer as compared to other stochastic algorithms because it provides superior results in terms of resolving stagnation in local optima and convergence speed problems. Besides, it gives the best results for breast cancer, heart, sine function and sigmoid function datasets as compared to other participating algorithms. Moreover, CPSOGSA also provides very competitive results for other datasets.

Originality/value

The CPSOGSA performed effectively in overcoming stagnation in local optima problem and increasing the overall convergence speed of MLP. Basically, CPSOGSA is a hybrid optimization algorithm which has powerful characteristics of global exploration capability and high local exploitation power. In the research literature, a little work is available where CPSO and GSA have been utilized for training MLP. The only related research paper was given by Mirjalili et al., in 2012. They have used standard PSO and GSA for training simple FNNs. However, the work employed only three datasets and used the MSE performance metric for evaluating the efficiency of the algorithms. In this paper, eight different standard datasets and five performance metrics have been utilized for investigating the efficiency of CPSOGSA in training MLPs. In addition, a non-parametric pair-wise statistical test namely the Wilcoxon rank-sum test has been carried out at a 5% significance level to statistically validate the simulation results. Besides, eight state-of-the-art meta-heuristic algorithms were employed for comparative analysis of the experimental results to further raise the authenticity of the experimental setup.

Details

International Journal of Intelligent Computing and Cybernetics, vol. 13 no. 2
Type: Research Article
ISSN: 1756-378X

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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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