• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 1998.10a
• /
• pp.553-559
• /
• 1998

Simple Genetic Algorithm(SGA) proposed by J. H. Holland is a population-based optimization method based on the principle of the Darwinian natural selection. The theoretical foundations of GA are the Schema Theorem and the Building Block Hypothesis. Although GA goes well in many applications as an optimization method, still it does not guarantee the convergence to a global optimum in some problems. In designing intelligent systems, specially, since there is no deterministic solution, a heuristic trial-and error procedure is usually used to determine the systems' parameters. As an alternative scheme, therefore, there is a growing interest in a co-evolutionary system, where two populations constantly interact and co-evolve. In this paper we review the existing co-evolutionary algorithms and propose co-evolutionary schemes designing intelligent systems according to the relation between the system's components.

• Transactions of the Korean Society of Mechanical Engineers A
• /
• v.27 no.9
• /
• pp.1463-1468
• /
• 2003

This paper presented the real-time self-tuning learning control based on evolutionary computation, which proves its superiority in finding of the optimal solution at the off-line learning method. The individuals of the populations are reduced in order to learn the evolutionary strategy in real-time, and new method that guarantee the convergence of evolutionary mutations is proposed. It is possible to control the control object slightly varied as time changes. As the state value of the control object is generated, evolutionary strategy is applied each sampling time because the learning process of an estimation, selection, mutation is done in real-time. These algorithms can be applied; the people who do not have knowledge about the technical tuning of dynamic systems could design the controller or problems in which the characteristics of the system dynamics are slightly varied as time changes.

• Proceedings of the Computational Structural Engineering Institute Conference
• /
• 1996.10a
• /
• pp.315-321
• /
• 1996

The structural topology optimization presented in this paper is based on an evolutionary procedure, developed by Xie and Steven, in which the low stressed material of a structure is removed from the structure step-by-step until an optimal design is obtained. By appling this procedure a layout or topology of a structure can be found from a initial block of material. The purpose of this paper is to implement the evolutionary procedure, introduce some novel features and investigate its feasibility by studying a few examples.

• Proceedings of the Korea Society for Simulation Conference
• /
• 1997.04a
• /
• pp.124-127
• /
• 1997

A key element in the control of Automated Guided Vehicle Systems (AGVS) is dispatching policy. This paper proposes a new dispatching algorithm for an efficient operation of AGVS. Based on an evolutionary operation, it has an adaptive control capability responding to changes of the system environment. The performance of the algorithm is compared with some well-known dispatching rules in terms of the system throughput through simulation. Sensitivity analysis is carried out varying the buffer capacity and the number of AGVS.

• IE interfaces
• /
• v.17 no.2
• /
• pp.208-217
• /
• 2004

This paper deals with a process planning problem in the flexible assembly system (FAS). The problem is to assign assembly tasks to stations with limited working space and to determine assembly routing with the objective of minimizing transfer time of the products among stations, while satisfying precedence relations among the tasks and upper-bound workload constraints for each station. In the process planning of FAS, the optimality of assembly routing depends on tasks loading. The integration of tasks loading and assembly routing is therefore important for an efficient utilization of FAS. To solve the integrated problem at the same time, in this paper we propose a new method using an artificial intelligent search technique, named 2-leveled symbiotic evolutionary algorithm. Through computational experiments, the performance of the proposed algorithm is compared with those of a traditional evolutionary algorithm and a symbiotic evolutionary algorithm. The experimental results show that the proposed algorithm outperforms the algorithms compared.

• Industrial Engineering and Management Systems
• /
• v.14 no.1
• /
• pp.44-57
• /
• 2015

In recent years, for the purpose of solving the problem regarding environment protection and resource saving, certain measures and policies have been promoted to establish a reverse supply chains (RSCs) with material flows from collection of used products to reuse the recycled parts in production of products. It is necessary to analyze behaviors of RSC members to determine the optimal operation. This paper discusses a RSC with a retailer and a manufacturer and verifies the behavior strategies of RSC members which may change over time in response to changes parameters related to the recycling promotion activity in RSC. A retailer takes two behaviors: cooperation/non-cooperation in recycling promotion activity. A manufacturer takes two behaviors: monitoring/non-monitoring of behaviors of the retailer. Evolutionary game theory combining the evolutionary theory of Darwin with game theory is adopted to clarify analytically evolutionary outcomes driven by a change in each behavior of RSC members over time. The evolutionary stable strategies (ESSs) for RSC members' behaviors are derived by using the replicator dynamics. The analysis numerically demonstrates how parameters of the recycling promotion activity: (i) sale promotion cost, (ii) monitoring cost, (iii) compensation and (iv) penalty cost affect the judgment of ESSs of behaviors of RSC members.

• 한국가스학회:학술대회논문집
• /
• 1998.09a
• /
• pp.13-18
• /
• 1998

• Journal of Electrical Engineering and Technology
• /
• v.3 no.1
• /
• pp.43-51
• /
• 2008

Evolutionary Algorithms (EAs) cover all the applications involving the use of Evolutionary Computation in electronic system design. It is largely applied to complex optimization problems. EAs introduce a new idea for automatic design of electronic systems; instead of imagine model, ions, and conventional techniques, it uses search algorithm to design a circuit. In this paper, a method for automatic optimization of the digital circuit design method has been introduced. This method is based on randomized search techniques mimicking natural genetic evolution. The proposed method is an iterative procedure that consists of a constant-size population of individuals, each one encoding a possible solution in a given problem space. The structure of the circuit is encoded into a one-dimensional genotype as represented by a finite string of bits. A number of bit strings is used to represent the wires connection between the level and 7 types of possible logic gates; XOR, XNOR, NAND, NOR, AND, OR, NOT 1, and NOT 2. The structure of gates are arranged in an $m{\times}n$ matrix form in which m is the number of input variables.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.11 no.7
• /
• pp.3501-3523
• /
• 2017

It is a classical integer optimization difficulty to design an optimal selection scheme in cooperative relay networks considering co-channel interference (CCI). In this paper, we solve single-objective and multi-objective relay selection problem. For the single-objective relay selection problem, in order to attain optimal system performance of cooperative relay network, a novel quantum differential evolutionary algorithm (QDEA) is proposed to resolve the optimization difficulty of optimal relay selection, and the proposed optimal relay selection scheme is called as optimal relay selection based on quantum differential evolutionary algorithm (QDEA). The proposed QDEA combines the advantages of quantum computing theory and differential evolutionary algorithm (DEA) to improve exploring and exploiting potency of DEA. So QDEA has the capability to find the optimal relay selection scheme in cooperative relay networks. For the multi-objective relay selection problem, we propose a novel non-dominated sorting quantum differential evolutionary algorithm (NSQDEA) to solve the relay selection problem which considers two objectives. Simulation results indicate that the proposed relay selection scheme based on QDEA is superior to other intelligent relay selection schemes based on differential evolutionary algorithm, artificial bee colony optimization and quantum bee colony optimization in terms of convergence speed and accuracy for the single-objective relay selection problem. Meanwhile, the simulation results also show that the proposed relay selection scheme based on NSQDEA has a good performance on multi-objective relay selection.

• Structural Engineering and Mechanics
• /
• v.4 no.5
• /
• pp.513-527
• /
• 1996

In this paper, both an approximate expression and an exact expression for the contribution factor of an element to the natural frequency of the finite element discretized system of a structure in general and a membrane in particular have been derived from the energy conservation principle and the finite element formulation of structural eigenvalue problems. The approximate expression for the contribution factor of an element is used to predict and determine the elements to be removed in an iteration since it depends only on the quantities associated with the old system in the iteration. The exact expression for the contribution factor of an element makes it possible to check whether the element is correctly removed at the end of an iteration because it depends on both the old system and the new system in the iteration. Thus, the combined use of the approximate expression and the exact expression allows a considerable number of elements to be removed in a single iteration so that the efficiency of the evolutionary structural optimization method can be greatly improved for solving the natural frequency optimization problem of a structure. A square membrane with different boundary supports has been chosen to investigate the general evolutionary path for the fundamental natural frequency of the structure. The related results indicated that if the objective of a structural optimization is to raise the fundamental natural frequency of the structure to an optimal value, the general evolutionary path during its optimization is that the elements are gradually removed along the direction from the area surrounded by the contour of the highest value to that surrounded by the contour of the lowest value.