Search results

1 – 10 of over 2000
Article
Publication date: 1 February 1999

HASHEM AL‐TABTABAI and ALEX P. ALEX

Genetic algorithm (GA) is a model of machine learning. The algorithm can be used to find sub‐optimum, if not optimum, solution(s) to a particular problem. It explores the solution…

Abstract

Genetic algorithm (GA) is a model of machine learning. The algorithm can be used to find sub‐optimum, if not optimum, solution(s) to a particular problem. It explores the solution space in an intelligent manner to evolve better solutions. The algorithm does not need any specific programming efforts but requires encoding the solution as strings of parameters. The field of application of genetic algorithms has increased dramatically in the last few years. A large variety of possible GA application tools now exist for non‐computer specialists. Complicated problems in a specific optimization domain can be tackled effectively with a very modest knowledge of the theory behind genetic algorithms. This paper reviews the technique briefly and applies it to solve some of the optimization problems addressed in construction management literature. The lessons learned from the application of GA to these problems are discussed. The result of this review is an indication of how the GA can contribute in solving construction‐related optimization problems. A summary of general guidelines to develop solutions using this optimization technique concludes the paper.

Details

Engineering, Construction and Architectural Management, vol. 6 no. 2
Type: Research Article
ISSN: 0969-9988

Keywords

Article
Publication date: 1 February 2018

Sangeetha M. and Sabari A.

This paper aims to provide a prolonging network lifetime and optimizing energy consumption in mobile wireless sensor networks (MWSNs). MWSNs have characteristics of dynamic…

Abstract

Purpose

This paper aims to provide a prolonging network lifetime and optimizing energy consumption in mobile wireless sensor networks (MWSNs). MWSNs have characteristics of dynamic topology due to the factors such as energy consumption and node movement that lead to create a problem in lifetime of the sensor network. Node clustering in wireless sensor networks (WSNs) helps in extending the network life time by reducing the nodes’ communication energy and balancing their remaining energy. It is necessary to have an effective clustering algorithm for adapting the topology changes and improve the network lifetime.

Design/methodology/approach

This work consists of two centralized dynamic genetic algorithm-constructed algorithms for achieving the objective in MWSNs. The first algorithm is based on improved Unequal Clustering-Genetic Algorithm, and the second algorithm is Hybrid K-means Clustering-Genetic Algorithm.

Findings

Simulation results show that improved genetic centralized clustering algorithm helps to find the good cluster configuration and number of cluster heads to limit the node energy consumption and enhance network lifetime.

Research limitations/implications

In this work, each node transmits and receives packets at the same energy level throughout the solution. The proposed approach was implemented in centralized clustering only.

Practical implications

The main reason for the research efforts and rapid development of MWSNs occupies a broad range of circumstances in military operations.

Social implications

The research highly gains impacts toward mobile-based applications.

Originality/value

A new fitness function is proposed to improve the network lifetime, energy consumption and packet transmissions of MWSNs.

Details

Sensor Review, vol. 38 no. 4
Type: Research Article
ISSN: 0260-2288

Keywords

Article
Publication date: 1 August 2002

Laura Núñez‐Letamendia

Outlines the development of genetic algorithms (GA), explains how they generate solutions to problems and applies four GA models incorporating different factors (e.g. risk…

Abstract

Outlines the development of genetic algorithms (GA), explains how they generate solutions to problems and applies four GA models incorporating different factors (e.g. risk, transaction costs etc.) to financial investment strategies. Uses 1987‐1996 share price data from the Madrid Stock Exchange (Spain) and a buy‐and‐hold strategy in the IBEX‐35 index as a benchmark. Shows that all four GA models generat superior daily returns of long positions with lower risk; and discusses the variations between them in detail.

Details

Managerial Finance, vol. 28 no. 8
Type: Research Article
ISSN: 0307-4358

Keywords

Article
Publication date: 26 September 2008

C.Y. Lam, S.L. Chan, W.H. Ip and C.W. Lau

The aim of this paper is to propose a genetic algorithms approach to develop a collaborative supply chain network, i.e. a supply chain network with genetic algorithms embedded (GA

1323

Abstract

Purpose

The aim of this paper is to propose a genetic algorithms approach to develop a collaborative supply chain network, i.e. a supply chain network with genetic algorithms embedded (GA‐SCN), so as to increase the efficiency and effectiveness of a supply chain network.

Design/methodology/approach

The methodologies of the GA‐SCN are illustrated through a case study of a supply chain network of a Hong Kong lamp manufacturing company involving 10 entities, whose roles range from suppliers, purchasers, designers and manufacturers, to sales and distributors. A GA‐SCN is developed according to the information provided by the company, the performance results in the case study are discussed, and the concepts of network analysis are then introduced to analyze the equivalence structure of the developed GA‐SCN.

Findings

The genetic algorithms approach is a suitable approach for developing an efficient and effective supply chain network in terms of shortening the processing time and reducing operating time in the network: the processing time and operating cost are reduced by around 45 percent and 35 percent per order, respectively, in the case study.

Originality/value

This paper is the first known study to apply genetic algorithms for the development of a collaborative supply chain network to increase the competitiveness of a supply chain.

Details

Industrial Management & Data Systems, vol. 108 no. 8
Type: Research Article
ISSN: 0263-5577

Keywords

Open Access
Article
Publication date: 20 July 2020

Mehmet Fatih Uslu, Süleyman Uslu and Faruk Bulut

Optimization algorithms can differ in performance for a specific problem. Hybrid approaches, using this difference, might give a higher performance in many cases. This paper…

1325

Abstract

Optimization algorithms can differ in performance for a specific problem. Hybrid approaches, using this difference, might give a higher performance in many cases. This paper presents a hybrid approach of Genetic Algorithm (GA) and Ant Colony Optimization (ACO) specifically for the Integrated Process Planning and Scheduling (IPPS) problems. GA and ACO have given different performances in different cases of IPPS problems. In some cases, GA has outperformed, and so do ACO in other cases. This hybrid method can be constructed as (I) GA to improve ACO results or (II) ACO to improve GA results. Based on the performances of the algorithm pairs on the given problem scale. This proposed hybrid GA-ACO approach (hAG) runs both GA and ACO simultaneously, and the better performing one is selected as the primary algorithm in the hybrid approach. hAG also avoids convergence by resetting parameters which cause algorithms to converge local optimum points. Moreover, the algorithm can obtain more accurate solutions with avoidance strategy. The new hybrid optimization technique (hAG) merges a GA with a local search strategy based on the interior point method. The efficiency of hAG is demonstrated by solving a constrained multi-objective mathematical test-case. The benchmarking results of the experimental studies with AIS (Artificial Immune System), GA, and ACO indicate that the proposed model has outperformed other non-hybrid algorithms in different scenarios.

Details

Applied Computing and Informatics, vol. 18 no. 1/2
Type: Research Article
ISSN: 2210-8327

Keywords

Article
Publication date: 15 May 2019

Xiabao Huang and Lixi Yang

Flexible job-shop scheduling is significant for different manufacturing industries nowadays. Moreover, consideration of transportation time during scheduling makes it more…

1054

Abstract

Purpose

Flexible job-shop scheduling is significant for different manufacturing industries nowadays. Moreover, consideration of transportation time during scheduling makes it more practical and useful. The purpose of this paper is to investigate multi-objective flexible job-shop scheduling problem (MOFJSP) considering transportation time.

Design/methodology/approach

A hybrid genetic algorithm (GA) approach is integrated with simulated annealing to solve the MOFJSP considering transportation time, and an external elitism memory library is employed as a knowledge library to direct GA search into the region of better performance.

Findings

The performance of the proposed algorithm is tested on different MOFJSP taken from literature. Experimental results show that proposed algorithm performs better than the original GA in terms of quality of solution and distribution of the solution, especially when the number of jobs and the flexibility of the machine increase.

Originality/value

Most of existing studies have not considered the transportation time during scheduling of jobs. The transportation time is significantly desired to be included in the FJSP when the time of transportation of jobs has significant impact on the completion time of jobs. Meanwhile, GA is one of primary algorithms extensively used to address MOFJSP in literature. However, to solve the MOFJSP, the original GA has a possibility to get a premature convergence and it has a slow convergence speed. To overcome these problems, a new hybrid GA is developed in this paper.

Details

International Journal of Intelligent Computing and Cybernetics, vol. 12 no. 2
Type: Research Article
ISSN: 1756-378X

Keywords

Article
Publication date: 27 November 2020

Bahman Arasteh, Razieh Sadegi and Keyvan Arasteh

Software module clustering is one of the reverse engineering techniques, which is considered to be an effective technique for presenting software architecture and structural…

Abstract

Purpose

Software module clustering is one of the reverse engineering techniques, which is considered to be an effective technique for presenting software architecture and structural information. The objective of clustering software modules is to achieve minimum coupling among different clusters and create maximum cohesion among the modules of each cluster. Finding the best clustering is considered to be a multi-objective N-P hard optimization-problem, and for solving this problem, different meta-heuristic algorithms have been previously proposed. Achieving higher module lustering quality (MQ), obtaining higher success rate for achieving the best clustering quality and improving convergence speed are the main objectives of this study.

Design/methodology/approach

In this study, a method (Bölen) is proposed for clustering software modules which combines the two algorithms of shuffled frog leaping and genetic algorithm.

Findings

The results of conducted experiments using traditional data sets confirm that the proposed method outperforms the previous methods in terms of convergence speed, module clustering quality and stability of the results.

Originality/value

The study proposes SFLA_GA algorithm for optimizing software module clustering, implementing SFLA algorithm in a discrete form by two operators of the genetic algorithm and achieving the above-mentioned purposes in this study. The aim is to achieve higher performance of the proposed algorithm in comparison with other algorithms.

Details

Data Technologies and Applications, vol. 55 no. 2
Type: Research Article
ISSN: 2514-9288

Keywords

Article
Publication date: 1 April 1996

ALEXANDER M. ROBERTSON and PETER WILLETT

This paper describes the development of a genetic algorithm (GA) for the assignment of weights to query terms in a ranked‐output document retrieval system. The GA involves a…

Abstract

This paper describes the development of a genetic algorithm (GA) for the assignment of weights to query terms in a ranked‐output document retrieval system. The GA involves a fitness function that is based on full relevance information, and the rankings resulting from the use of these weights are compared with the Robertson‐Sparck Jones F4 retrospective relevance weight. Extended experiments with seven document test collections show that the ga can often find weights that are slightly superior to those produced by the deterministic weighting scheme. That said, there are many cases where the two approaches give the same results, and a few cases where the F4 weights are superior to the ga weights. Since the ga has been designed to identify weights yielding the best possible level of retrospective performance, these results indicate that the F4 weights provide an excellent and practicable alternative. Evidence is presented to suggest that negative weights may play an important role in retrospective relevance weighting.

Details

Journal of Documentation, vol. 52 no. 4
Type: Research Article
ISSN: 0022-0418

Article
Publication date: 4 September 2019

Zineb Ibn Majdoub Hassani, Abdellah El Barkany, Abdelouahhab Jabri, Ikram El Abbassi and Abdel Moumen Darcherif

This paper aims to present a new model for solving the integrated production planning and scheduling. Usually, the two decision levels are treated sequentially because of their…

Abstract

Purpose

This paper aims to present a new model for solving the integrated production planning and scheduling. Usually, the two decision levels are treated sequentially because of their complexity. Scheduling depends on the lot sizes calculated at the tactical level and ignoring scheduling constraints generates unrealistic and inconsistent decisions. Therefore, integrating more detail scheduling constraint in production planning is important for managing efficiently operations. Therefore, an integrated model was developed, and two evolutionary optimization approaches were suggested for solving it, namely, genetic algorithm (GA) and the hybridization of simulated annealing (SA) with GA HSAGA. The proposed algorithms have some parameters that must be adjusted using Taguchi method. Therefore, to evaluate the proposed algorithm, the authors compared the results given by GA and the hybridization. The SA-based local search is embedded into a GA search mechanism to move the GA away from being closed within local optima. The analysis shows that the combination of simulated annealing with GA gives better solutions and minimizes the total production costs.

Design/methodology/approach

The paper opted for an approached resolution method particularly GA and simulated annealing. The study represents a comparison between the results found using GA and the hybridization of simulated annealing and GA. A total of 45 instances were studied to evaluate job-shop problems of different sizes.

Findings

The results illustrate that for 36 instances of 45, the hybridization of simulated annealing and GA HSAGA has provided best production costs. The efficiency demonstrated by HSAGA approach is related to the combination between the exploration ability of GA and the capacity to escape local optimum of simulated annealing.

Originality/value

This study provides a new resolution approach to the integration of planning and scheduling while considering a new operational constrain. The model suggested aims to control the available capacity of the resources and guaranties that the resources to be consumed do not exceed the real availability to avoid the blocking that results from the unavailability of resources. Furthermore, to solve the MILP model, a GA is proposed and then it is combined to simulated annealing.

Details

Journal of Engineering, Design and Technology , vol. 18 no. 1
Type: Research Article
ISSN: 1726-0531

Keywords

Article
Publication date: 1 January 2001

K.C. LAM, TIE SONG HU, THOMAS NG, R.K.K. YUEN, S.M. LO and CONRAD T.C. WONG

Optimizing both qualitative and quantitative factors is a key challenge in solving construction finance decisions. The semi‐structured nature of construction finance optimization…

Abstract

Optimizing both qualitative and quantitative factors is a key challenge in solving construction finance decisions. The semi‐structured nature of construction finance optimization problems precludes conventional optimization techniques. With a desire to improve the performance of the canonical genetic algorithm (CGA) which is characterized by static crossover and mutation probability, and to provide contractors with a profit‐risk trade‐off curve and cash flow prediction, an adaptive genetic algorithm (AGA) model is developed. Ten projects being undertaken by a major construction firm in Hong Kong were used as case studies to evaluate the performance of the genetic algorithm (GA). The results of case study reveal that the AGA outperformed the CGA both in terms of its quality of solutions and the computational time required for a certain level of accuracy. The results also indicate that there is a potential for using the GA for modelling financial decisions should both quantitative and qualitative factors be optimized simultaneously.

Details

Engineering, Construction and Architectural Management, vol. 8 no. 1
Type: Research Article
ISSN: 0969-9988

Keywords

1 – 10 of over 2000