• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2007.04a
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• pp.233-236
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• 2007

A new evolution method for strong exploration and strong exploitation termed queen-bee and mutant-bee evolution is proposed based on the previous queen-bee evolution [1]. Even though the queen-bee evolution has shown very good performances, two parameters for strong mutation are added to the genetic algorithms. This makes the application of genetic algorithms with queen-bee evolution difficult because the values of the two parameters are empirically decided by a trial-and-error method without a systematic method. The queen-bee and mutant-bee evolution has no this problem because it does not need additional parameters for strong mutation. Experimental results with typical problems showed that the queen-bee and mutant-bee evolution produced nearly similar results to the best ones of queen-bee evolution even though it didn't need to select proper values of additional parameters.

• Journal of Sensor Science and Technology
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• v.14 no.6
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• pp.402-408
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• 2005

This paper describes the optimal design of a multi-layered surface plasmon resonance sensors to meet various specifications and improve some physical parameters. Dip 3 dB bandwidth and depth were chosen as design parameters and the objective function was the norm of the difference between design parameters and target values. The design variables are thicknesses of each layer and to obtain the design parameters, the optical admittance method was employed. The (1+1) evolution strategy was employed as an optimization tool. By applying the proposed optimization procedure to a 3-layered sensor, the optimized design variables considerably improved the 3 dB bandwidth by 4.8 nm and the dip depth by 1.1 dB.

• Proceedings of the KIEE Conference
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• 1995.11a
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• pp.9-11
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• 1995

In this paper, tile squirrel case induction motors required multi-objective function are designed. As the objective function of the optimization program, we select the linear combination of loss and mass of motors by using weighting factors. Optimization process is performed by using the evolution strategy (ES). ES is the algorithm that can find the global minimum. To verify validity of the proposed method, a sample design is tried.

• Advances in Computational Design
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• v.1 no.3
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• pp.275-296
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• 2016

This article presents an adaptive directional differential evolution (ADDE) algorithm and its application in solving discrete sizing truss optimization problems. The algorithm is featured by a new self-adaptation approach and a simple directional strategy. In the adaptation approach, the mutation operator is adjusted in accordance with the change of population diversity, which can well balance between global exploration and local exploitation as well as locate the promising solutions. The directional strategy is based on the order relation between two difference solutions chosen for mutation and can bias the search direction for increasing the possibility of finding improved solutions. In addition, a new scaling factor is introduced as a vector of uniform random variables to maintain the diversity without crossover operation. Numerical results show that the optimal solutions of ADDE are as good as or better than those from some modern metaheuristics in the literature, while ADDE often uses fewer structural analyses.

• KIEE International Transaction on Electrical Machinery and Energy Conversion Systems
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• v.2B no.4
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• pp.156-161
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• 2002

This paper proposes an optimized universal motor for improving its performance using the finite element method (FEM) with the (1＋1) Evolution Strategy (ES) algorithm. To do this, various design parameters are modified, such as air gap length, shape of motor slot, pole shoe, pole width, and rotor shaft diameter. Two parameters (arc length of stator pole and thickness of pole shoe) are chosen and optimized using the program, and the optimized model is built and tested with a performance measuring system. The measured values of the model are compared with those of the initial and the optimized model to prove the algorithm. As a result, the final model improves its performance compared with those of the initial model.

• Proceedings of the KIEE Conference
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• 1996.11a
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• pp.6-8
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• 1996

The multiobjective optimization is presented for the optimal design of induction motors. The aim of design is to find an optimized induction motor in terms of both the efficiency and the mass. The efficiency and the mass are linearly combined using the weighting factors. Optimization process is performed by using the improved (1+1) evolution strategy (ES). ES is the algorithm that can find the global minimum. To verify the validity of the proposed method. the method is applied to a sample design.

• Proceedings of the KIEE Conference
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• 1994.11a
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• pp.130-132
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• 1994

Recently, a Deflection Yoke(DY) is designed in the bobbin-seperator-coil-winding type for high-definite CRT and high-efficient DY of wide vision TV or High Definite TV. This paper presents an optimization or bobbin-seperator-coil-winding type yoke's coil distribution for minimizing gap between desired and practical deflections of electron beams using by Evolution Strategy.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.11
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• pp.5574-5591
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• 2017

Inspired by the idea of Artificial Immune System, many researches of wireless sensor network (WSN) intrusion detection is based on the artificial intelligent system (AIS). However, a large number of generated detectors, black hole, overlap problem of NSA have impeded further used in WSN. In order to improve the anomaly detection performance for WSN, detector generation mechanism need to be improved. Therefore, in this paper, a Differential Evolution Constraint Multi-objective Optimization Problem based Negative Selection Algorithm (DE-CMOP based NSA) is proposed to optimize the distribution and effectiveness of the detector. By combining the constraint handling and multi-objective optimization technique, the algorithm is able to generate the detector set with maximized coverage of non-self space and minimized overlap among detectors. By employing differential evolution, the algorithm can reduce the black hole effectively. The experiment results show that our proposed scheme provides improved NSA algorithm in-terms, the detectors generated by the DE-CMOP based NSA more uniform with less overlap and minimum black hole, thus effectively improves the intrusion detection performance. At the same time, the new algorithm reduces the number of detectors which reduces the complexity of detection phase. Thus, this makes it suitable for intrusion detection in WSN.

• Structural Engineering and Mechanics
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• v.44 no.3
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• pp.379-403
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• 2012

In recent years, the need for optimal design of structures under time-history loading aroused great attention in researchers. The main problem in this field is the extremely high computational demand of time-history analyses, which may convert the solution algorithm to an illogical one. In this paper, a new framework is developed to solve the size optimization problem of steel truss structures subjected to ground motions. In order to solve this problem, the covariance matrix adaptation evolution strategy algorithm is employed for the optimization procedure, while a generalized regression neural network is utilized as a meta-model for fitness approximation. Moreover, the computational cost of time-history analysis is decreased through a wavelet analysis. Capability and efficiency of the proposed framework is investigated via two design examples, comprising of a tower truss and a footbridge truss.

• Journal of Power Electronics
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• v.18 no.1
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• pp.92-102
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• 2018

A sliding mode control (SMC) for servo motors based on the differential evolution (DE) algorithm, called DE-SMC, is proposed in this study. The parameters of SMC should be designed exactly to improve the robustness, realize the precision positioning, and reduce the steady-state speed error of the servo drive. The main parameters of SMC are optimized using the DE algorithm according to the speed feedback information of the servo motor. The most significant influence factor of the DE algorithm is optimization iteration. A suitable iteration can be achieved by the tested optimization process profile of the main parameters of SMC. Once the parameters of SMC are optimized under a convergent iteration, the system realizes the given performance indices within the shortest time. The experiment indicates that the robustness of the system is improved, and the dynamic and steady performance achieves the given performance indices under a convergent iteration when motor parameters mismatch and load disturbance is added. Moreover, the suitable iteration effectively mitigates the low-speed crawling phenomenon in the system. The correctness and effectiveness of DE-SMC are verified through the experiment.