Millie Pant - Profile on Academia.edu (original) (raw)
Papers by Millie Pant
World Journal of Modelling and …, Jan 1, 2010
Differential Evolution (DE) is a stochastic, population based search technique, which can be clas... more Differential Evolution (DE) is a stochastic, population based search technique, which can be classified as an Evolutionary Algorithm (EA) using the concepts of selection crossover and reproduction to guide the search. It has emerged as a powerful tool for solving optimization problems in the past few years. However, the convergence rate of DE still does not meet all the requirements, and attempts to speed up differential evolution are considered necessary. In order to improve the performance of DE, We propose two versions of (DE) called Differential Evolution with Parent Centric Crossover (DEPCX) and Differential Evolution with probabilistic Parent Centric Crossover (Pro. DEPCX). The proposed algorithms are validated on a test bed of seven real life, nonlinear engineering design problems and numerical results are compared with original differential evolution (DE). Empirical analysis of the results indicates that the proposed schemes enhance the performance of basic DE in terms of convergence rate without compromising with the quality of solution.
An improved differential evolution algorithm with fitness-based adaptation of the control parameters
Information Sciences, 2011
Differential evolution (DE) is a reliable and versatile function optimiser especially suited for ... more Differential evolution (DE) is a reliable and versatile function optimiser especially suited for continuous optimisation problems. Practical experience, however, shows that DE easily looses diversity and is susceptible to premature and/or slow convergence. This paper proposes a modified variant of DE algorithm called improved differential evolution (IDE). It works in three phases: decentralisation, evolution and centralisation of the population. Initially, the individuals of the population are partitioned into several groups of ...
… , 2009. CEC'09. IEEE Congress on, Jan 1, 2009
Differential evolution (DE) is a powerful yet simple evolutionary algorithm for optimizing real v... more Differential evolution (DE) is a powerful yet simple evolutionary algorithm for optimizing real valued optimization problems. Traditional investigations with differential evolution have used a single mutation operator. Using a variety of mutation operators that can be integrated during evolution could hold the potential to generate a better solution with less computational effort. In view of this, in this paper a mixed mutation strategy which uses the concept of evolutionary game theory is proposed to integrate basic differential evolution mutation and quadratic interpolation to generate a new solution. Throughout this paper we refer this new algorithm as, differential evolution with mixed mutation strategy (MSDE). The performance of proposed algorithm is investigated and compared with basic differential evolution. The experiments conducted shows that proposed algorithm outperform the basic DE algorithm in all the benchmark problems.
Mathematical Problems in Engineering, 2013
The "trim loss problem" (TLP) is one of the most challenging problems in context of optimization ... more The "trim loss problem" (TLP) is one of the most challenging problems in context of optimization research. It aims at determining the optimal cutting pattern of a number of items of various lengths from a stock of standard size material to meet the customers' demands that the wastage due to trim loss is minimized. The resulting mathematical model is highly nonconvex in nature accompanied with several constraints with added restrictions of binary variables. This prevents the application of conventional optimization methods. In this paper we use synergetic differential evolution (SDE) for the solution of this type of problems. Four hypothetical but relevant cases of trim loss problem arising in paper industry are taken for the experiment. The experimental results compared with those of the other techniques show the competence of the SDE to solve the problem.
Differential Evolution Using Interpolated Local Search
Communications in Computer and Information Science, 2010
ABSTRACT In this paper we propose a novel variant of the Differential Evolution (DE) algorithm ba... more ABSTRACT In this paper we propose a novel variant of the Differential Evolution (DE) algorithm based on local search. The corresponding algorithm is named as Differential Evolution with Interpolated Local Search (DEILS). In DEILS, the local search operation is applied in an adaptive manner. The adaptive behavior enables the algorithm to search its neighborhood in an effective manner and the interpolation helps in exploiting the solutions. In this way a balance is maintained between the exploration and exploitation factors. The performance of DEILS is investigated and compared with basic differential evolution, modified versions of DE and some other evolutionary algorithms. It is found that the proposed scheme improves the performance of DE in terms of quality of solution without compromising with the convergence rate.
Inserting information sharing mechanism of PSO to improve the convergence of DE
2009 World Congress on Nature & Biologically Inspired Computing (NaBIC), 2009
... However DE faces criticism regarding its convergence rate which sometimes slows as it approac... more ... However DE faces criticism regarding its convergence rate which sometimes slows as it approaches ... in the sense that it uses same evolutionary operators like mutation, crossover and selection ... Nevertheless, it's the application of these operators that makes DE different from GA. ...
A Modified Differential Evolution Algorithm with Cauchy Mutation for Global Optimization
Contemporary Computing, Jan 1, 2009
Abstract. Differential Evolution (DE) is a powerful yet simple evolutionary algorithm for optimiz... more Abstract. Differential Evolution (DE) is a powerful yet simple evolutionary algorithm for optimization of real valued, multi modal functions. DE is generally considered as a reliable, accurate and robust optimization technique. However, the algorithm suffers from premature ...
Applied Soft Computing, 2014
The multi-level image thresholding is often treated as a problem of optimization. Typically, find... more The multi-level image thresholding is often treated as a problem of optimization. Typically, finding the parameters of these problems leads to a nonlinear optimization problem, for which obtaining the solution is computationally expensive and time-consuming. In this paper a new multi-level image thresholding technique using synergetic differential evolution (SDE), an advanced version of differential evolution (DE), is proposed. SDE is a fusion of three algorithmic concepts proposed in modified versions of DE. It utilizes two criteria (1) entropy and (2) approximation of normalized histogram of an image by a mixture of Gaussian distribution to find the optimal thresholds. The experimental results show that SDE can make optimal thresholding applicable in case of multi-level thresholding and the performance is better than some other multi-level thresholding methods.
An image watermarking scheme in wavelet domain with optimized compensation of singular value decomposition via artificial bee colony
Information Sciences, 2015
ABSTRACT Digital image watermarking is the process of authenticating a digital image by embedding... more ABSTRACT Digital image watermarking is the process of authenticating a digital image by embedding a watermark into it and thereby protecting the image from copyright infringement. This paper proposes a novel robust image watermarking scheme developed in the wavelet domain based on the singular value decomposition (SVD) and artificial bee colony (ABC) algorithm. The host image is transformed into an invariant wavelet domain by applying redistributed invariant wavelet transform, subsequently the low frequency sub-band of wavelet transformed image is segmented into non-overlapping blocks. The most suitable embedding blocks are selected using the human visual system for the watermark embedding. The watermark bits are embedded into the target blocks by modifying the first column coefficients of the left singular vector matrix of SVD decomposition with the help of a threshold and the visible distortion caused by the embedding is compensated by modifying the coefficients of the right singular vector matrix employing compensation parameters. Furthermore, ABC is employed to obtain the optimized threshold and compensation parameters. Experimental results, compared with the related existing schemes, demonstrated that the proposed scheme not only possesses the strong robustness against image manipulation attacks, but also, is comparable to other schemes in term of visual quality.
Differential Evolution Using Mixed Strategies in Competitive Environment
International journal of innovative computing, information & control: IJICIC
Musrrat Ali1, Millie Pant1, Ajith Abraham2 and Vaclav Snasel3 1Department of Paper Technology, In... more Musrrat Ali1, Millie Pant1, Ajith Abraham2 and Vaclav Snasel3 1Department of Paper Technology, Indian Institute of Technology Roorkee, Roorkee 247667, India. {musrrat.iitr, millidma}@gmail.com ... 2 Machine Intelligence Research Labs (MIR Labs), Scientific ...
Improving differential evolution algorithm by synergizing different improvement mechanisms
ACM Transactions on Autonomous and Adaptive Systems, 2012
ABSTRACT Differential Evolution (DE) is a well-known Evolutionary Algorithm (EA) for solving glob... more ABSTRACT Differential Evolution (DE) is a well-known Evolutionary Algorithm (EA) for solving global optimization problems. Practical experiences, however, show that DE is vulnerable to problems like slow and/or premature convergence. In this article we propose a simple and modified DE framework, called MDE, which is a fusion of three recent modifications in DE: (1) Opposition-Based Learning (OBL); (2) tournament method for mutation; and (3) single population structure. These features have a specific role which helps in improving the performance of DE. While OBL helps in giving a good initial start to DE, the use of the tournament best base vector in the mutation phase helps in preserving the diversity. Finally the single population structure helps in faster convergence. Their synergized effect balances the exploitation and exploration capabilities of DE without compromising with the solution quality or the convergence rate. The proposed MDE is validated on a set of 25 standard benchmark problems, 7 nontraditional shifted benchmark functions proposed at the special session of CEC2008, and three engineering design problems. Numerical results and statistical analysis show that the proposed MDE is better than or at least comparable to the basic DE and several other state-of-the art DE variants.
A robust image watermarking technique using SVD and differential evolution in DCT domain
Optik - International Journal for Light and Electron Optics, 2014
ABSTRACT
Cuckoo search algorithm for the selection of optimal machining parameters in milling operations
The International Journal of Advanced Manufacturing Technology, 2013
Abstract In this research, a new optimization algorithm, called the cuckoo search algorithm (CS) ... more Abstract In this research, a new optimization algorithm, called the cuckoo search algorithm (CS) algorithm, is introduced for solving manufacturing optimization problems. This research is the first application of the CS to the optimization of machining parameters in the literature. In order to demonstrate the effectiveness of the CS, a milling optimization problem was solved and the results were compared with those obtained using other well-known optimization techniques like, ant colony algorithm, immune algorithm, hybrid immune ...
Interpolated differential evolution for global optimisation problems
International Journal of Computing Science and Mathematics, 2010
... In a short span of around 15 years, it has emerged as a powerful optimisation tool and has be... more ... In a short span of around 15 years, it has emerged as a powerful optimisation tool and has been successfully applied to a wide range of problems (Wang and Cheng, 1999; Babu and Munawar, 2000; Babu and Singh, 2000; Angira and Babu, 2005, 2006; Babu and Angira, 2001 ...
European Journal of Operational Research, 2011
In the present study, a modified variant of Differential Evolution (DE) algorithm for solving mul... more In the present study, a modified variant of Differential Evolution (DE) algorithm for solving multi-objective optimization problems is presented. The proposed algorithm, named Multi-Objective Differential Evolution Algorithm (MODEA) utilizes the advantages of Opposition-Based Learning for generating an initial population of potential candidates and the concept of random localization in mutation step. Finally, it introduces a new selection mechanism for generating a well distributed Pareto optimal front. The performance of proposed algorithm is investigated on a set of nine bi-objective and five tri-objective benchmark test functions and the results are compared with some recently modified versions of DE for MOPs and some other Multi Objective Evolutionary Algorithms (MOEAs). The empirical analysis of the numerical results shows the efficiency of the proposed algorithm.
A simplex differential evolution algorithm: development and applications
Transactions of the Institute of Measurement and Control, 2011
Abstract Population-based heuristic optimization methods like differential evolution (DE) depend ... more Abstract Population-based heuristic optimization methods like differential evolution (DE) depend largely on the generation of the initial population. The initial population not only affects the search for several iterations but often also has an influence on the final solution. The ...
International Journal of Bio-Inspired …, Jan 1, 2011
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Differential evolution with parent centric crossover
Second UKSIM European Symposium on …, Jan 1, 2008
Differential Evolution (DE) has emerged as a powerful tool for solving optimization problems in t... more Differential Evolution (DE) has emerged as a powerful tool for solving optimization problems in the last few years. However, the convergence rate of DE still does not meet all the requirements, and attempts to speed up differential evolution are considered necessary. ...
An optimized watermarking technique based on DE in DWT-SVD domain
2013 IEEE Symposium on Differential Evolution (SDE), 2013
ABSTRACT
2009 World Congress on Nature & Biologically Inspired Computing (NaBIC), 2009
Differential Evolution (DE) is generally considered as a reliable, accurate and robust optimizati... more Differential Evolution (DE) is generally considered as a reliable, accurate and robust optimization technique. However, the algorithm suffers from slow convergence rate and takes large computational time for optimizing the computationally expensive objective functions. Therefore, an attempt to speed up DE is considered necessary. This research introduces a modified differential evolution, called Ant Colony Differential Evolution, ACDE. The ACDE algorithm initializes the population using opposition based learning, in mutation phase it applies random localization technique and it simulates the movement of ants to refine the best solution found in each generation. Also, it maintains a single set of population while updating the population for next generation. ACDE validated on a test bed of 7 benchmark problems and two real life problems and the numerical results are compared with original DE. It is found that ACDE requires less computational effort to locate global optimal solution without compromising with the quality of solution.
World Journal of Modelling and …, Jan 1, 2010
Differential Evolution (DE) is a stochastic, population based search technique, which can be clas... more Differential Evolution (DE) is a stochastic, population based search technique, which can be classified as an Evolutionary Algorithm (EA) using the concepts of selection crossover and reproduction to guide the search. It has emerged as a powerful tool for solving optimization problems in the past few years. However, the convergence rate of DE still does not meet all the requirements, and attempts to speed up differential evolution are considered necessary. In order to improve the performance of DE, We propose two versions of (DE) called Differential Evolution with Parent Centric Crossover (DEPCX) and Differential Evolution with probabilistic Parent Centric Crossover (Pro. DEPCX). The proposed algorithms are validated on a test bed of seven real life, nonlinear engineering design problems and numerical results are compared with original differential evolution (DE). Empirical analysis of the results indicates that the proposed schemes enhance the performance of basic DE in terms of convergence rate without compromising with the quality of solution.
An improved differential evolution algorithm with fitness-based adaptation of the control parameters
Information Sciences, 2011
Differential evolution (DE) is a reliable and versatile function optimiser especially suited for ... more Differential evolution (DE) is a reliable and versatile function optimiser especially suited for continuous optimisation problems. Practical experience, however, shows that DE easily looses diversity and is susceptible to premature and/or slow convergence. This paper proposes a modified variant of DE algorithm called improved differential evolution (IDE). It works in three phases: decentralisation, evolution and centralisation of the population. Initially, the individuals of the population are partitioned into several groups of ...
… , 2009. CEC'09. IEEE Congress on, Jan 1, 2009
Differential evolution (DE) is a powerful yet simple evolutionary algorithm for optimizing real v... more Differential evolution (DE) is a powerful yet simple evolutionary algorithm for optimizing real valued optimization problems. Traditional investigations with differential evolution have used a single mutation operator. Using a variety of mutation operators that can be integrated during evolution could hold the potential to generate a better solution with less computational effort. In view of this, in this paper a mixed mutation strategy which uses the concept of evolutionary game theory is proposed to integrate basic differential evolution mutation and quadratic interpolation to generate a new solution. Throughout this paper we refer this new algorithm as, differential evolution with mixed mutation strategy (MSDE). The performance of proposed algorithm is investigated and compared with basic differential evolution. The experiments conducted shows that proposed algorithm outperform the basic DE algorithm in all the benchmark problems.
Mathematical Problems in Engineering, 2013
The "trim loss problem" (TLP) is one of the most challenging problems in context of optimization ... more The "trim loss problem" (TLP) is one of the most challenging problems in context of optimization research. It aims at determining the optimal cutting pattern of a number of items of various lengths from a stock of standard size material to meet the customers' demands that the wastage due to trim loss is minimized. The resulting mathematical model is highly nonconvex in nature accompanied with several constraints with added restrictions of binary variables. This prevents the application of conventional optimization methods. In this paper we use synergetic differential evolution (SDE) for the solution of this type of problems. Four hypothetical but relevant cases of trim loss problem arising in paper industry are taken for the experiment. The experimental results compared with those of the other techniques show the competence of the SDE to solve the problem.
Differential Evolution Using Interpolated Local Search
Communications in Computer and Information Science, 2010
ABSTRACT In this paper we propose a novel variant of the Differential Evolution (DE) algorithm ba... more ABSTRACT In this paper we propose a novel variant of the Differential Evolution (DE) algorithm based on local search. The corresponding algorithm is named as Differential Evolution with Interpolated Local Search (DEILS). In DEILS, the local search operation is applied in an adaptive manner. The adaptive behavior enables the algorithm to search its neighborhood in an effective manner and the interpolation helps in exploiting the solutions. In this way a balance is maintained between the exploration and exploitation factors. The performance of DEILS is investigated and compared with basic differential evolution, modified versions of DE and some other evolutionary algorithms. It is found that the proposed scheme improves the performance of DE in terms of quality of solution without compromising with the convergence rate.
Inserting information sharing mechanism of PSO to improve the convergence of DE
2009 World Congress on Nature & Biologically Inspired Computing (NaBIC), 2009
... However DE faces criticism regarding its convergence rate which sometimes slows as it approac... more ... However DE faces criticism regarding its convergence rate which sometimes slows as it approaches ... in the sense that it uses same evolutionary operators like mutation, crossover and selection ... Nevertheless, it's the application of these operators that makes DE different from GA. ...
A Modified Differential Evolution Algorithm with Cauchy Mutation for Global Optimization
Contemporary Computing, Jan 1, 2009
Abstract. Differential Evolution (DE) is a powerful yet simple evolutionary algorithm for optimiz... more Abstract. Differential Evolution (DE) is a powerful yet simple evolutionary algorithm for optimization of real valued, multi modal functions. DE is generally considered as a reliable, accurate and robust optimization technique. However, the algorithm suffers from premature ...
Applied Soft Computing, 2014
The multi-level image thresholding is often treated as a problem of optimization. Typically, find... more The multi-level image thresholding is often treated as a problem of optimization. Typically, finding the parameters of these problems leads to a nonlinear optimization problem, for which obtaining the solution is computationally expensive and time-consuming. In this paper a new multi-level image thresholding technique using synergetic differential evolution (SDE), an advanced version of differential evolution (DE), is proposed. SDE is a fusion of three algorithmic concepts proposed in modified versions of DE. It utilizes two criteria (1) entropy and (2) approximation of normalized histogram of an image by a mixture of Gaussian distribution to find the optimal thresholds. The experimental results show that SDE can make optimal thresholding applicable in case of multi-level thresholding and the performance is better than some other multi-level thresholding methods.
An image watermarking scheme in wavelet domain with optimized compensation of singular value decomposition via artificial bee colony
Information Sciences, 2015
ABSTRACT Digital image watermarking is the process of authenticating a digital image by embedding... more ABSTRACT Digital image watermarking is the process of authenticating a digital image by embedding a watermark into it and thereby protecting the image from copyright infringement. This paper proposes a novel robust image watermarking scheme developed in the wavelet domain based on the singular value decomposition (SVD) and artificial bee colony (ABC) algorithm. The host image is transformed into an invariant wavelet domain by applying redistributed invariant wavelet transform, subsequently the low frequency sub-band of wavelet transformed image is segmented into non-overlapping blocks. The most suitable embedding blocks are selected using the human visual system for the watermark embedding. The watermark bits are embedded into the target blocks by modifying the first column coefficients of the left singular vector matrix of SVD decomposition with the help of a threshold and the visible distortion caused by the embedding is compensated by modifying the coefficients of the right singular vector matrix employing compensation parameters. Furthermore, ABC is employed to obtain the optimized threshold and compensation parameters. Experimental results, compared with the related existing schemes, demonstrated that the proposed scheme not only possesses the strong robustness against image manipulation attacks, but also, is comparable to other schemes in term of visual quality.
Differential Evolution Using Mixed Strategies in Competitive Environment
International journal of innovative computing, information & control: IJICIC
Musrrat Ali1, Millie Pant1, Ajith Abraham2 and Vaclav Snasel3 1Department of Paper Technology, In... more Musrrat Ali1, Millie Pant1, Ajith Abraham2 and Vaclav Snasel3 1Department of Paper Technology, Indian Institute of Technology Roorkee, Roorkee 247667, India. {musrrat.iitr, millidma}@gmail.com ... 2 Machine Intelligence Research Labs (MIR Labs), Scientific ...
Improving differential evolution algorithm by synergizing different improvement mechanisms
ACM Transactions on Autonomous and Adaptive Systems, 2012
ABSTRACT Differential Evolution (DE) is a well-known Evolutionary Algorithm (EA) for solving glob... more ABSTRACT Differential Evolution (DE) is a well-known Evolutionary Algorithm (EA) for solving global optimization problems. Practical experiences, however, show that DE is vulnerable to problems like slow and/or premature convergence. In this article we propose a simple and modified DE framework, called MDE, which is a fusion of three recent modifications in DE: (1) Opposition-Based Learning (OBL); (2) tournament method for mutation; and (3) single population structure. These features have a specific role which helps in improving the performance of DE. While OBL helps in giving a good initial start to DE, the use of the tournament best base vector in the mutation phase helps in preserving the diversity. Finally the single population structure helps in faster convergence. Their synergized effect balances the exploitation and exploration capabilities of DE without compromising with the solution quality or the convergence rate. The proposed MDE is validated on a set of 25 standard benchmark problems, 7 nontraditional shifted benchmark functions proposed at the special session of CEC2008, and three engineering design problems. Numerical results and statistical analysis show that the proposed MDE is better than or at least comparable to the basic DE and several other state-of-the art DE variants.
A robust image watermarking technique using SVD and differential evolution in DCT domain
Optik - International Journal for Light and Electron Optics, 2014
ABSTRACT
Cuckoo search algorithm for the selection of optimal machining parameters in milling operations
The International Journal of Advanced Manufacturing Technology, 2013
Abstract In this research, a new optimization algorithm, called the cuckoo search algorithm (CS) ... more Abstract In this research, a new optimization algorithm, called the cuckoo search algorithm (CS) algorithm, is introduced for solving manufacturing optimization problems. This research is the first application of the CS to the optimization of machining parameters in the literature. In order to demonstrate the effectiveness of the CS, a milling optimization problem was solved and the results were compared with those obtained using other well-known optimization techniques like, ant colony algorithm, immune algorithm, hybrid immune ...
Interpolated differential evolution for global optimisation problems
International Journal of Computing Science and Mathematics, 2010
... In a short span of around 15 years, it has emerged as a powerful optimisation tool and has be... more ... In a short span of around 15 years, it has emerged as a powerful optimisation tool and has been successfully applied to a wide range of problems (Wang and Cheng, 1999; Babu and Munawar, 2000; Babu and Singh, 2000; Angira and Babu, 2005, 2006; Babu and Angira, 2001 ...
European Journal of Operational Research, 2011
In the present study, a modified variant of Differential Evolution (DE) algorithm for solving mul... more In the present study, a modified variant of Differential Evolution (DE) algorithm for solving multi-objective optimization problems is presented. The proposed algorithm, named Multi-Objective Differential Evolution Algorithm (MODEA) utilizes the advantages of Opposition-Based Learning for generating an initial population of potential candidates and the concept of random localization in mutation step. Finally, it introduces a new selection mechanism for generating a well distributed Pareto optimal front. The performance of proposed algorithm is investigated on a set of nine bi-objective and five tri-objective benchmark test functions and the results are compared with some recently modified versions of DE for MOPs and some other Multi Objective Evolutionary Algorithms (MOEAs). The empirical analysis of the numerical results shows the efficiency of the proposed algorithm.
A simplex differential evolution algorithm: development and applications
Transactions of the Institute of Measurement and Control, 2011
Abstract Population-based heuristic optimization methods like differential evolution (DE) depend ... more Abstract Population-based heuristic optimization methods like differential evolution (DE) depend largely on the generation of the initial population. The initial population not only affects the search for several iterations but often also has an influence on the final solution. The ...
International Journal of Bio-Inspired …, Jan 1, 2011
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Differential evolution with parent centric crossover
Second UKSIM European Symposium on …, Jan 1, 2008
Differential Evolution (DE) has emerged as a powerful tool for solving optimization problems in t... more Differential Evolution (DE) has emerged as a powerful tool for solving optimization problems in the last few years. However, the convergence rate of DE still does not meet all the requirements, and attempts to speed up differential evolution are considered necessary. ...
An optimized watermarking technique based on DE in DWT-SVD domain
2013 IEEE Symposium on Differential Evolution (SDE), 2013
ABSTRACT
2009 World Congress on Nature & Biologically Inspired Computing (NaBIC), 2009
Differential Evolution (DE) is generally considered as a reliable, accurate and robust optimizati... more Differential Evolution (DE) is generally considered as a reliable, accurate and robust optimization technique. However, the algorithm suffers from slow convergence rate and takes large computational time for optimizing the computationally expensive objective functions. Therefore, an attempt to speed up DE is considered necessary. This research introduces a modified differential evolution, called Ant Colony Differential Evolution, ACDE. The ACDE algorithm initializes the population using opposition based learning, in mutation phase it applies random localization technique and it simulates the movement of ants to refine the best solution found in each generation. Also, it maintains a single set of population while updating the population for next generation. ACDE validated on a test bed of 7 benchmark problems and two real life problems and the numerical results are compared with original DE. It is found that ACDE requires less computational effort to locate global optimal solution without compromising with the quality of solution.