• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.11
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• pp.5489-5511
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• 2019

The performance of evolutionary algorithms can be seriously weakened when constraints limit the feasible region of the search space. In this paper we present a constrained multi-objective optimization algorithm based on adaptive ε-truncation (ε-T-CMOA) to further improve distribution and convergence of the obtained solutions. First of all, as a novel constraint handling technique, ε-truncation technique keeps an effective balance between feasible solutions and infeasible solutions by permitting some excellent infeasible solutions with good objective value and low constraint violation to take part in the evolution, so diversity is improved, and convergence is also coordinated. Next, an exponential variation is introduced after differential mutation and crossover to boost the local exploitation ability. At last, the improved crowding density method only selects some Pareto solutions and near solutions to join in calculation, thus it can evaluate the distribution more accurately. The comparative results with other state-of-the-art algorithms show that ε-T-CMOA is more diverse than the other algorithms and it gains better in terms of convergence in some extent.

• Journal of Power Electronics
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• v.16 no.3
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• pp.1097-1109
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• 2016

Fault detection and isolation are related to system monitoring, identifying when a fault has occurred, and determining the type of fault and its location. Fault detection is utilized to determine whether a problem has occurred within a certain channel or area of operation. Fault detection and diagnosis have become increasingly important for many technical processes in the development of safe and efficient advanced systems for supervision. This paper presents an integrated technique for fault diagnosis and classification for open- and short-circuit faults in three-phase inverter circuits. Discrete wavelet transform and principal component analysis are utilized to detect the discontinuity in currents caused by a fault. The features of fault diagnosis are then extracted. A fault dictionary is used to acquire details about transistor faults and the corresponding fault identification. Fault classification is performed with a fuzzy logic system and relevance vector machine (RVM). The proposed model is incorporated with a set of optimization techniques, namely, evolutionary particle swarm optimization (EPSO) and cuckoo search optimization (CSO), to improve fault detection. The combination of optimization techniques with classification techniques is analyzed. Experimental results confirm that the combination of CSO with RVM yields better results than the combinations of CSO with fuzzy logic system, EPSO with RVM, and EPSO with fuzzy logic system.

• Food Science and Biotechnology
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• v.17 no.5
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• pp.899-903
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• 2008

This study was conducted in order to elucidate the optimum conditions for the extraction of clove that can be used to elicit antibacterial activity against Streptococcus mutans using the evolutionary operation (EVOP)-factorial design technique. Higher antibacterial activity was achieved in a higher extraction temperature of $80^{\circ}C$ ($r=0.7983^{**}$) and in a longer extraction time of 26 hr ($r=0.6867^*$). Antibacterial activity was not effected by differentiation of ethanol concentration in the extraction solvent (r=-0.0683). The maximum antibacterial activity of clove against S. mutans as determined by the EVOP-factorial design technique was obtained at an extraction temperature of $80^{\circ}C$, an extraction time of 26 hr and a 50% ethanol concentration. Furthermore, the population of S. mutans decreased from an initial concentration of 6.850 to 4.195 log CFU/mL in the third set that is more than 2.6 log cycles by EVOP-factorial design technique.

• Journal of Microbiology and Biotechnology
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• v.17 no.11
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• pp.1880-1884
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• 2007

This study was conducted to find the optimum extraction condition of Gold-Thread for antibacterial activity against Streptococcus mutans using The evolutionary operation-factorial design technique. Higher antibacterial activity was achieved in a higher extraction temperature ($R^2=-0.79$) and in a longer extraction time ($R^2=-0.71$). Antibacterial activity was not affected by differentiation of the ethanol concentration in the extraction solvent ($R^2=-0.12$). The maximum antibacterial activity of clove against S. mutans determined by the EVOP-factorial technique was obtained at $80^{\circ}C$ extraction temperature, 26 h extraction time, and 50% ethanol concentration. The population of S. mutans decreased from 6.110 logCFU/ml in the initial set to 4.125 logCFU/ml in the third set.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.2
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• pp.36-42
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• 2021

This paper deals with a vehicle routing problem with resource repositioning (VRPRR) which is a variation of well-known vehicle routing problem with pickup and delivery (VRPPD). VRPRR in which static repositioning of public bikes is a representative case, can be defined as a multi-objective optimization problem aiming at minimizing both transportation cost and the amount of unmet demand. To obtain Pareto sets for the problem, famous multi-objective optimization algorithms such as Strength Pareto Evolutionary Algorithm 2 (SPEA2) can be applied. In addition, a linear combination of two objective functions with weights can be exploited to generate Pareto sets. By varying weight values in the combined single objective function, a set of solutions is created. Experiments accomplished with a standard benchmark problem sets show that Variable Neighborhood Search (VNS) applied to solve a number of single objective function outperforms SPEA2. All generated solutions from SPEA2 are completely dominated by a set of VNS solutions. It seems that local optimization technique inherent in VNS makes it possible to generate near optimal solutions for the single objective function. Also, it shows that trade-off between the number of solutions in Pareto set and the computation time should be considered to obtain good solutions effectively in case of linearly combined single objective function.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.50 no.7
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• pp.318-323
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• 2001

This paper proposes an efficient method of On-Line Economic Load Dispatch(ELD) using Evolutionary Programming(EP) technique at KwangYang steel works. The economic operation of steel works is done by experienced engineer. So far, there is no formulation and algorithm of economic operation of steel works. In this paper, we propose the formulation and On-Line algorithm of economic operation of steel works. The EP technique is the kind of Evolutionary Computation(EC), which has become a candidate for many optimization applications due to its flexibility and efficiency. Based on the EP technique, the proposed algorithm is capable of not only solving the economic problem, but also obtaining the global optimal solutions within reasonable execution time. To validate our proposed approach, the algorithm is demonstrated on many sample systems and compared to actual values of steel works.

• Proceedings of the KIEE Conference
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• 2000.11a
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• pp.116-118
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• 2000

This paper proposes an efficient method of On-Line Economic Load Dispatch(ELD) using Evolutionary Programming(EP) technique at Kwangyang steel works. The economic operation of steel works is done by experienced engineer. So far, there is no formulation and algorithm of economic operation of steel works. In this paper, we propose the formulation and On-Line algorithm of economic operation of steel works. The EP technique is the kind of Evolutionary Computation(EC) which has become a candidate for many optimization applications due to its flexibility and efficiency. Based on the EP technique, the proposed algorithm is capable of not only solving the economic problem, but also obtaining the global optimal solutions within reasonable execution time. To validate our proposed approach, the algorithm is demonstrated on many sample systems and compared to actual values of steel works.

• Advances in Computational Design
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• v.4 no.3
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• pp.197-210
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• 2019

Motion generation of a four-bar linkage is a type of mechanism synthesis that has a wide range of applications such as a pick-and-place operation in manufacturing. In this research, the use of meta-heuristics for motion generation of a four-bar linkage is demonstrated. Three problems of motion generation were posed as a constrained optimization probably using the weighted sum technique to handle two types of tracking errors. A simple penalty function technique was used to deal with design constraints while three meta-heuristics including differential evolution (DE), self-adaptive differential evolution (JADE) and teaching learning based optimization (TLBO) were employed to solve the problems. Comparative results and the effect of the constraint handling technique are illustrated and discussed.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.4
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• pp.328-338
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• 2006

A new evolutionary computation technique, called particle swarm optimization(PSO), has been proposed and introduced recently. PSO has been inspired by the social behavior of flocking organisms, such as swarms of birds and fish schools and PSO is an algorithm that follows a collaborative population-based search model. Each particle of swarm flies around in a multidimensional search space looking for the optimal solution. Then, Particles adjust their position according to their own and their neighboring-particles experience. In this paper, characteristics of PSO such as mentioned are reviewed and compared with GA which is based on the evolutionary mechanism in natural selection. Also dimensionalities of PSO and GA are compared throughout numeric experimental studies. The comparative studies demonstrate that PSO is characterized as simple in concept, easy to implement, and computationally efficient and can generate a high-quality solution and stable convergence characteristic than GA.

• Magazine of the Korean Society of Agricultural Engineers
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• v.44 no.2
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• pp.127-135
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• 2002

Due to the fact that the design of a reinforced concrete structure changes in accordance with its shape and assigned load, total automation of the design system has not been achieved. For instance, since there is no general rule about setting up reinforcing steel quantity and arrangement location, it is simply not feasible to automatically decide the reinforcing arrangement location. In this study, the ESO(evolutionary structural optimization) technique and its related issues will be discussed. The ESO techniques is determined the reasonable load path which is traveling of load between in-flow and out-flow at a concrete structure using numerical analysis. And the results applied to the steel arrangement in reinforced concrete structures. The optimal algorithm, which determines the terminal criteria during ESO process, has been updated by using the obtained results. And the load path within the member has been determined automatically.