• International Journal of Control, Automation, and Systems
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• v.2 no.2
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• pp.247-255
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• 2004

The majority of real-world problems encountered by engineers involve simultaneous optimization of competing objectives. In this case instead of single optima, there is a set of alternative trade-offs, generally known as Pareto-optimal solutions. The use of evolutionary algorithms Pareto GA, which was first introduced by Goldberg in 1989, has now become a sort of standard in solving Multiobjective Optimization Problems (MOPs). Though this approach was further developed leading to numerous applications, these applications are based on Pareto ranking and employ the use of the fitness sharing function to maintain diversity. Another scheme for solving MOPs has been presented by J. Nash to solve MOPs originated from Game Theory and Economics. Sefrioui introduced the Nash Genetic Algorithm in 1998. This approach combines genetic algorithms with Nash's idea. Another central achievement of Game Theory is the introduction of an Evolutionary Stable Strategy, introduced by Maynard Smith in 1982. In this paper, we will try to find ESS as a solution of MOPs using our game model based co-evolutionary algorithm. First, we will investigate the validity of our co-evolutionary approach to solve MOPs. That is, we will demonstrate how the evolutionary game can be embodied using co-evolutionary algorithms and also confirm whether it can reach the optimal equilibrium point of a MOP. Second, we will evaluate the effectiveness of our approach, comparing it with other methods through rigorous experiments on several MOPs.

• Journal of Korean Association for Spatial Structures
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• v.2 no.3 s.5
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• pp.93-102
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• 2002

The objective of this study is the development of size, shape and topology discrete optimum design algorithm which is based on the genetic algorithms. The algorithm can perform both shape and topology optimum designs of trusses. The developed algorithm was implemented in a computer program. For the optimum design, the objective function is the weight of trusses and the constraints are stress and displacement. The basic search method for the optimum design is the genetic algorithms. The algorithm is known to be very efficient for the discrete optimization. The genetic algorithm consists of genetic process and evolutionary process. The genetic process selects the next design points based on the survivability of the current design points. The evolutionary process evaluates the survivability of the design points selected from the genetic process. The efficiency and validity of the developed size, shape and topology discrete optimum design algorithms were verified by applying the algorithm to optimum design examples

• 한국공간정보시스템학회:학술대회논문집
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• 2004.05a
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• pp.43-52
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• 2004

The objective of this study is the development of size, shape and topology discrete optimum design algorithm which is based on the genetic algorithms. The algorithm can perform both shape and topology optimum designs of trusses. The developed algerian was implemented in a computer program. For the optimum design, the objective function is the weight of trusses and the constraints are stress and displacement. The basic search method for the optimum design is the genetic algorithms. The algorithm is known to be very efficient for the discrete optimization. The genetic algorithm consists of genetic process and evolutionary process. The genetic process selects the next design points based on the survivability of the current design points. The evolutionary process evaluates the survivability of the design points selected from the genetic process. The efficiency and validity of the developed size, shape and topology discrete optimum design algorithms were verified by applying the algorithm to optimum design examples

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.2
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• pp.206-211
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• 2011

In this paper, the evolutionary algorithm was specifically formulated for optimization with continuous parameter space. The proposal was motivated by the fact that the genetic algorithms have been most intensively reported for parameter identification problems with continuous search space. The difference of primary characteristics between genetic algorithms and the proposed algorithm, discrete or continuous individual representation has made different areas to which the algorithms should be applied. Results obtained by optimization of some well-known test functions indicate that the proposed algorithm is superior to genetic algorithms in all the performance, computation time and memory usage for continuous search space problems.

• Industrial Engineering and Management Systems
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• v.11 no.3
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• pp.215-223
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• 2012

This paper focuses on three very similar evolutionary algorithms: genetic algorithm (GA), particle swarm optimization (PSO), and differential evolution (DE). While GA is more suitable for discrete optimization, PSO and DE are more natural for continuous optimization. The paper first gives a brief introduction to the three EA techniques to highlight the common computational procedures. The general observations on the similarities and differences among the three algorithms based on computational steps are discussed, contrasting the basic performances of algorithms. Summary of relevant literatures is given on job shop, flexible job shop, vehicle routing, location-allocation, and multimode resource constrained project scheduling problems.

• Smart Structures and Systems
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• v.8 no.4
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• pp.343-365
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• 2011

Structural system identification (SSI) is an inverse problem of difficult solution. Currently, difficulties lie in the development of algorithms which can cater to large size problems. In this paper, a parameter estimation technique based on evolutionary strategy is presented to overcome some of the difficulties encountered in using the traditional system identification methods in terms of convergence. In this paper, a non-traditional form of system identification technique employing evolutionary algorithms is proposed. In order to improve the convergence characteristics, it is proposed to employ immune algorithms which are proved to be built with superior diversification mechanism than the conventional evolutionary algorithms and are being used for several practical complex optimisation problems. In order to reduce the number of design variables, domain decomposition methods are used, where the identification process of the entire structure is carried out in multiple stages rather than in single step. The domain decomposition based methods also help in limiting the number of sensors to be employed during dynamic testing of the structure to be identified, as the process of system identification is carried out in multiple stages. A fifteen storey framed structure, truss bridge and 40 m tall microwave tower are considered as a numerical examples to demonstrate the effectiveness of the domain decomposition based structural system identification technique using immune algorithm.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.51 no.11
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• pp.544-550
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• 2002

This paper describes a strategy of a daily optimal operational scheduling on the industrial cogeneration system. The cogeneration system selected to establish the scheduling consists of three units and several auxiliary devices which include three auxiliary boilers, t재 waste boilers and three sludge incinerators. One unit generated electrical and thermal energy using the back pressure turbine. The other two units generate the energy using the extraction condensing turbine. Three auxiliary devices operate to supply energy to the loads with three units. The cogeneration system is able to supply enough the thermal energy to the thermal load, however it can not sufficiently supply the electric energy to the electrical load. Therefore the insufficient electric energy is compensated by buying electrical energy from utility. In this paper, the evolutionary algorithms was applied to establish the optimal scheduling for the cogeneration systems. Also the GUI System was developed using established mathematics medeling and evolutionary algorithms in order that non-experts are able to establish operational scheduling. This results revel that the proposed modeling and strategy can be effectively applied to cogeneration system for paper mill.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.9
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• pp.3293-3311
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• 2015

Cloud services are required to be composed as a single service to fulfill the workflow applications. Service composition in Cloud raises new challenges caused by the diversity of users with different QoS requirements and vague preferences, as well as the development of cloud computing having geographically distributed characteristics. So the selection of the best service composition is a complex problem and it faces trade-off among various QoS criteria. In this paper, we propose a Cloud service composition approach based on evolutionary algorithms, i.e., NSGA-II and MOPSO. We utilize the combination of multi-objective evolutionary approaches and Decision-Making method (AHP) to solve Cloud service composition optimization problem. The weights generated from AHP are applied to the Crowding Distance calculations of the above two evolutionary algorithms. Our algorithm beats single-objective algorithms on the optimization ability. And compared with general multi-objective algorithms, it is able to precisely capture the users' preferences. The results of the simulation also show that our approach can achieve a better scalability.

• Journal of Korean Institute of Industrial Engineers
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• v.31 no.1
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• pp.79-86
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• 2005

The transportation problem (TP) is one of the traditional optimization problems. Unlike the TP, the fixed charge transportation problem (FCTP) cannot be solved using polynomial time algorithms. So, finding solutions for the FCTP is a well-known NP-complete problem involving an importance in a transportation network design. So, it seems to be natural to use evolutionary algorithms for solving FCTP. And many evolutionary algorithms have tackled this problem and shown good performance. This paper introduces an efficient evolutionary algorithm for the FCTP. The proposed algorithm can always generate feasible solutions without any repair process using the random key representation. Especially, it can guide the search toward the basic solution. Finally, we performed comparisons with the previous results known on the benchmark instances and could confirm the superiority of the proposed algorithm.

• ETRI Journal
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• v.44 no.5
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• pp.805-815
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• 2022

The model-based evolutionary algorithms are divided into three groups: estimation of distribution algorithms, inverse modeling, and surrogate modeling. Existing inverse modeling is mainly applied to solve multi-objective optimization problems and is not suitable for many-objective optimization problems. Some inversed-model techniques, such as the inversed-model of multi-objective evolutionary algorithm, constructed from the Pareto front (PF) to the Pareto solution on nondominated solutions using a random grouping method and Gaussian process, were introduced. However, some of the most efficient inverse models might be eliminated during this procedure. Also, there are challenges, such as the presence of many local PFs and developing poor solutions when the population has no evident regularity. This paper proposes inverse modeling using random forest regression and uniform reference points that map all nondominated solutions from the objective space to the decision space to solve many-objective optimization problems. The proposed algorithm is evaluated using the benchmark test suite for evolutionary algorithms. The results show an improvement in diversity and convergence performance (quality indicators).