• Industrial Engineering and Management Systems
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• 제10권1호
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• pp.7-14
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• 2011

In solving the Job Shop Scheduling Problem, the best solution rarely is completely random; it follows one or more rules (heuristics). The Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Simulated Annealing, and Tabu search, which belong to the Evolutionary Computations Algorithms (ECs), are not efficient enough in solving this problem as they neglect all conventional heuristics and hence they need to be hybridized with different heuristics. In this paper a new algorithm titled "Shaking Optimization Algorithm" is proposed that follows the common methodology of the Evolutionary Computations while utilizing different heuristics during the evolution process of the solution. The results show that the proposed algorithm outperforms the GA, PSO, SA, and TS algorithms, while being a good competitor to some other hybridized techniques in solving a selected number of benchmark Job Shop Scheduling problems.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2001년도 ICCAS
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• pp.176.3-176
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• 2001

Hydraulic servo system has the characteristic of high power in itself, as combining its characteristics with excellent electro equipment that comes from the development of electronics, electro-hydraulic servo system is widely used in industry that are requested high precision and power Electro-hydraulic servo system is characteristic of very strong non-linearity in itself and it is mainly applied the field of the inner or outer fluctuating load or disturbance in industry. Evolutionary computation based on the natural evolutionary process may solve many engineering problems. Algorithms can represent the natural selection in crossovers, mutations, production of the offspring, selection, etc. Nature has already shown is the superiority through ...

• 대한임베디드공학회논문지
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• 제4권2호
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• pp.90-96
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• 2009

In the original model-based paradigm in the field of motion planning of robots, robots had to play the focal role of considering all situations under which they made decisions and operate. Such paradigm makes it difficult to respond efficiently to the dynamically shifting environment such as disaster area. In order to handle such a situation that may be changed dynamically, a technology that allows a dynamic execution of data transmission and physical/network connection between multiple robots based on scenarios is required. In this paper, we deal with evolutionary robots that adapt to any given environment and execute scenarios, specially focused on the development of a simulator to test the evolutionary process of cooperated multiple robots.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권11호
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• pp.5592-5609
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• 2017

JPEG steganography detection is an active research topic in the field of information hiding due to the wide use of JPEG image in social network, image-sharing websites, and Internet communication, etc. In this paper, a new steganalysis method for content-adaptive JPEG steganography is proposed by integrating the evolutionary feature selection and classifier ensemble selection. First, the whole framework of the proposed steganalysis method is presented and then the characteristic of the proposed method is analyzed. Second, the feature selection method based on genetic algorithm is given and the implement process is described in detail. Third, the method of classifier ensemble selection is proposed based on Pareto evolutionary optimization. The experimental results indicate the proposed steganalysis method can achieve a competitive detection performance by compared with the state-of-the-art steganalysis methods when used for the detection of the latest content-adaptive JPEG steganography algorithms.

• Structural Engineering and Mechanics
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• 제27권1호
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• pp.49-61
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• 2007

This paper presents a modified and improved bi-directional evolutionary structural optimization (BESO) method for topology optimization. A sensitivity filter which has been used in other optimization methods is introduced into BESO so that the design solutions become mesh-independent. To improve the convergence of the optimization process, the sensitivity number considers its historical information. Numerical examples show the effectiveness of the modified BESO method in obtaining convergent and mesh-independent solutions. A study of the effects of various BESO parameters on the solution is then conducted to determine the appropriate values for these parameters.

• Journal of Electrical Engineering and Technology
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• 제9권2호
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• pp.471-477
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• 2014

The novel permanent magnetic actuator (PMA) and its optimal design method were proposed in this paper. The proposed PMA is referred to as the separated permanent magnetic actuator (SPMA) and significantly superior in terms of its cost and performance level over a conventional PMA. The proposed optimal design method uses the evolutionary strategy algorithm (ESA), the kriging meta-model (KMM), and the multi-step optimization. The KMM can compensate the slow convergence of the ESA. The proposed multi-step optimization process, which separates the independent variables, can decrease time and increase the reliability for the optimal design result. Briefly, the optimization time and the poor reliability of the optimum are mitigated by the proposed optimization method.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2003년도 학술회의 논문집 정보 및 제어부문 B
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• pp.376-379
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• 2003

DNA computing based DNA sequence Is operated through the biology experiment. Biology experiment used as operator causes illegal reactions through shifted hybridization, mismatched hybridization, undesired hybridization of the DNA sequence. So, it is essential to design DNA sequence to minimize the potential errors. This paper proposes method of the DNA sequence generation based evolutionary operation processor. Genetic algorithm was used for evolutionary operation and extra hardware, namely genetic algorithm processor was implemented for solving repeated evolutionary process that causes much computation time. To show efficiency of the Proposed processor, excellent result is confirmed by comparing between fitness of the DNA sequence formed randomly and DNA sequence formed by genetic algorithm processor. Proposed genetic algorithm processor can reduce the time and expense for preparing DNA sequence that is essential in DNA computing. Also it can apply design of the oligomer for development of the DNA chip or oligo chip.

• 제어로봇시스템학회논문지
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• 제14권10호
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• pp.1038-1046
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• 2008

This paper presents a framework to generate human-like movements of a humanoid in real time using the movement primitive database of a human. The framework consists of two processes: 1) the offline motion imitation learning based on an Evolutionary Algorithm and 2) the online motion generation of a humanoid using the database updated bγ the motion imitation teaming. For the offline process, the initial database contains the kinetic characteristics of a human, since it is full of human's captured motions. The database then develops through the proposed framework of motion teaming based on an Evolutionary Algorithm, having the kinetic characteristics of a humanoid in aspect of minimal torque or joint jerk. The humanoid generates human-like movements far a given purpose in real time by linearly interpolating the primitive motions in the developed database. The movement of catching a ball was examined in simulation.

• 한국경영과학회지
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• 제35권3호
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• pp.25-43
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• 2010

We consider a multi-objective balancing and sequencing problem in mixed model assembly lines, which is important for an efficient use of the assembly lines. In this paper, we present a neighborhood symbiotic evolutionary algorithm to simultaneously solve the two problems of balancing and model sequencing under multiple objectives. We aim to find a set of well-distributed solutions close to the true Pareto optimal solutions for decision makers. The proposed algorithm has a two-leveled structure. At Level 1, two populations are operated : One consists of individuals each of which represents a partial solution to the balancing problem and the other consists of individuals for the sequencing problem. Level 2, which is an upper level, works one population whose individuals represent the combined entire solutions to the two problems. The process of Level 1 imitates a neighborhood symbiotic evolution and that of Level 2 simulates an endosymbiotic evolution together with an elitist strategy to promote the capability of solution search. The performance of the proposed algorithm is compared with those of the existing algorithms in convergence, diversity and computation time of nondominated solutions. The experimental results show that the proposed algorithm is superior to the compared algorithms in all the three performance measures.

• Journal of Ecology and Environment
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• 제33권4호
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• pp.275-288
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• 2010

Ecological modeling faces some unique problems in dealing with complex environment-organism relationships, making it one of the toughest domains that might be encountered by a modeler. Newer technologies and ecosystem modeling paradigms have recently been proposed, all as part of a broader effort to reduce the uncertainty in models arising from qualitative and quantitative imperfections in the ecological data. In this paper, evolutionary computation modeling approaches are introduced and proposed as useful modeling tools for ecosystems. The results of our case study support the applicability of an algal predictive model constructed via genetic programming. In conclusion, we propose that evolutionary computation may constitute a powerful tool for the modeling of highly complex objects, such as river ecosystems.