• Journal of Ship and Ocean Technology
• /
• v.9 no.4
• /
• pp.35-46
• /
• 2005

The performance of optimisation methods, based on penalty functions, is highly problem- dependent and many methods require additional tuning of some variables. This additional tuning is the influences of penalty coefficient, which depend strongly on the degree of constraint violation. Moreover, Binary-coded Genetic Algorithm (BGA) meets certain difficulties when dealing with continuous and/or discrete search spaces with large dimensions. With the above reasons, Real-coded Micro-Genetic Algorithm (R$\mu$GA) is proposed to find the global optimum of continuous and/or discrete nonlinear constrained engineering problems without handling any of penalty functions. R$\mu$GA can help in avoiding the premature convergence and search for global solution-spaces, because of its wide spread applicability, global perspective and inherent parallelism. The proposed R$\mu$GA approach has been demonstrated by solving three different engineering design problems. From the simulation results, it has been concluded that R$\mu$GA is an effective global optimisation tool for solving continuous and/or discrete nonlinear constrained real­world optimisation problems.

• Journal of Information Processing Systems
• /
• v.14 no.5
• /
• pp.1237-1253
• /
• 2018

Harmony search algorithm is an emerging meta-heuristic optimization algorithm, which is inspired by the music improvisation process and can solve different optimization problems. In order to further improve the performance of the algorithm, this paper proposes an improved harmony search algorithm. Key parameters including harmonic memory consideration (HMCR), pitch adjustment rate (PAR), and bandwidth (BW) are optimized as the number of iterations increases. Meanwhile, referring to the genetic algorithm, an improved method to generate a new crossover solutions rather than the traditional mechanism of improvisation. Four complex function optimization and pressure vessel optimization problems were simulated using the optimization algorithm of standard harmony search algorithm, improved harmony search algorithm and exploratory harmony search algorithm. The simulation results show that the algorithm improves the ability to find global search and evolutionary speed. Optimization effect simulation results are satisfactory.

• 제어로봇시스템학회:학술대회논문집
• /
• 1996.10b
• /
• pp.89-92
• /
• 1996

In this paper, we developed visual tracking algorithm using double active bar. The active bar model to represent the object can reduce the search space of energy surface and better performance than those of snake model. However, the contour will not find global equilibrium when driving force caused by image may be weak. To overcome this problem. Double active bar is proposed for finding the global minimum point without any dependence on initialization. To achieve the goal, an deformable model with two initial contours in attempted to search for a global minimum within two specific initial contours. This approach improve the performance of finding the contour of target. To evaluate the performance, some experiments are executed. We can achieved the good result for tracking a object on noisy image.

• The Transactions of the Korean Institute of Electrical Engineers A
• /
• v.52 no.6
• /
• pp.325-332
• /
• 2003

The loss minimization is one of the most important problems to save the operational cost in distribution systems. This paper presents an efficient method for optimal feeder reconfiguration of distribution systems. Chaos search algorithm (CSA) is used to reconfigure distribution systems so that active power losses are globally minimized with turning on/off sectionalizing switches. In optimization problem, the CSA searches the global optimal solution on the basis of regularity in chaotic motions and easily escapes from local or near optimal solution. The CSA is tested on 15 buses and 32 buses distribution systems, and the results indicate that it is able to determine appropriate switching options for global optimum reconfiguration.

• Journal of applied mathematics & informatics
• /
• v.24 no.1_2
• /
• pp.325-331
• /
• 2007

To the unconstrained programme of non-convex function, this article give a modified BFGS algorithm associated with the general line search model. The idea of the algorithm is to modify the approximate Hessian matrix for obtaining the descent direction and guaranteeing the efficacious of the new quasi-Newton iteration equation $B_{k+1}s_k=y^*_k,\;where\;y^*_k$ is the sum of $y_k\;and\;A_ks_k,\;and\;A_k$ is some matrix. The global convergence properties of the algorithm associating with the general form of line search is proved.

• Journal of the Korean Institute of Telematics and Electronics B
• /
• v.29B no.3
• /
• pp.6-15
• /
• 1992

This paper proposes a method for the global optimization of redundancy over the whole task period for a kinematically redundant robot. The necessary conditions based on the calculus of variations for an integral type cost criterion result in a second-order differential equation. For a cyclic task, the periodic boundary conditions due to conservativity requirements are discussed. We refine the two-point boundary value problem to an initial value adjustment problem and suggest a numerical search method for providing the conservative global optimal solution using the gradient projection method. Since the initial joint velocity is parameterized with the number of the redundancy, we only search the parameter value in the space of as many dimensions as the number of degrees of redundancy. We show through numerical examples that multiple nonhomotopic extremal solutions and the generality of the proposed method by considering the dynamics of a robot.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 2003.09a
• /
• pp.183-186
• /
• 2003

Hybrid genetic algorithms (HGAs) have been studied as various ways. These HGAs usually use both the global search property of genetic algorithm (GA) and the local search one of local search techniques. One of the general types, when constructing HGAs, is to incorporate a local search technique into GA loop, and then the local search technique is repeated as many iteration number as the loop. This paper proposes a new HGA with a conditional local search technique (c-HGA) that does not be repeated as many iteration number as GA loop. For effectiveness of the proposed c-HGA, a conventional HGA and GA are also suggested, and then these algorithms are compared with each other in numerical examples,

• 제어로봇시스템학회:학술대회논문집
• /
• 1995.10a
• /
• pp.14-17
• /
• 1995

Various methods and techniques have been proposed for solving optimization problems; the methods have been applied to various practical problems. However the methods have demerits. The demerits which should be covered are, for example, falling into local minima, or, a slow convergence speed to optimal points. In this paper, Likelihood Search Method (L.S.M.) is proposed for searching for a global optimum systematically and effectively in a single framework, which is not a combination of different methods. The L.S.M. is a sort of a random search method (R.S.M.) and thus can get out of local minima. However exploitation of gradient information makes the L.S.M. superior in convergence speed to the commonly used R.S.M..

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.56 no.9
• /
• pp.1521-1526
• /
• 2007

This paper presents the application of Adaptive Evolutionary Algorithm (AEA) to search an optimal setting of overcurrent relay coordination to protect ring distribution systems. The AEA takes the merits of both a genetic algorithm (GA) and an evolution strategy (ES) in an adaptive manner to use the global search capability of GA and the local search capability of ES. The overcurrent relay settings and coordination requirements are formulated into a set of constraint equations and an objective function is developed to manage the overcurrent relay settings by the Time Coordination Method. The domain of overcurrent relays coordination for the ring-fed distribution systems is a non-linear system with a lot of local optimum points and a highly constrained optimization problem. Thus conventional methods fail in searching for the global optimum. AEA is employed to search for the optimum relay settings with maximum satisfaction of coordination constraints. The simulation results show that the proposed method can optimize the overcurrent relay settings, reduce relay mis-coordinated operations, and find better optimal overcurrent relay settings than the present available methods.

• Journal of the Korean Society for Precision Engineering
• /
• v.14 no.12
• /
• pp.24-29
• /
• 1997

Recently, Genetic Algorithm(GA), which is a stochastic direct search strategy that mimics the process of genetic evolution, is widely adapted into a search procedure for structural optimization. Contrast to traditional optimal design techniques which use design sensitivity analysis results, GA is very simple in their algorithms and there is no need of continuity of functions(or functionals) any more in GA. So, they can be easily applicable to wide area of design optimization problems. Also, owing to multi-point search procedure, they have higher porbability of convergence to global optimum compared to traditional techniques which take one-point search method. The methods consist of three genetics opera- tions named selection, crossover and mutation. In this study, a method of finding the omtimum size and topology of drilling machine is proposed by using the GA, For rapid converge to optimum, elitist survival model,roulette wheel selection with limited candidates, and multi-point shuffle cross-over method are adapted. And pseudo object function, which is the combined form of object function and penalty function, is used to include constraints into fitness function. GA shows good results of weight reducing effect and convergency in optimal design of drilling machine.