• Journal of the Korean Operations Research and Management Science Society
• /
• v.35 no.3
• /
• pp.25-43
• /
• 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.

• International Journal of Computer Science & Network Security
• /
• v.21 no.3
• /
• pp.304-310
• /
• 2021

The paper presents problems of optimization of the synthesis of embedded systems, in particular Pareto optimization. The model of such a system for its design for high-level of abstract is based on the classic approach known from the theory of task scheduling, but it is significantly extended, among others, by the characteristics of tasks and resources as well as additional criteria of optimal system in scope structure and operation. The metaheuristic algorithm operating according to this model introduces a new approach to system synthesis, in which parallelism of task scheduling and resources partition is applied. An algorithm based on a genetic approach with simulated annealing and Boltzmann tournaments, avoids local minima and generates optimized solutions. Such a synthesis is based on the implementation of task scheduling, resources identification and partition, allocation of tasks and resources and ultimately on the optimization of the designed system in accordance with the optimization criteria regarding cost of implementation, execution speed of processes and energy consumption by the system during operation. This paper presents examples and results for multi-criteria optimization, based on calculations for specifying non-dominated solutions and indicating a subset of Pareto solutions in the space of all solutions.

• The KIPS Transactions:PartB
• /
• v.11B no.7 s.96
• /
• pp.841-848
• /
• 2004

Evolutionary a1gorithms are well-suited for multi-objective optimization problems involving several, often conflicting objectives. Pareto-based evolutionary algorithms, in particular, have shown better performance than other multi-objective evolutionary algorithms in comparison. However, generalized evolutionary multi-objective optimization algorithms have a weak point, in which the distribution of solutions are not uni-formly distributed onto Pareto optimal front. In this paper, we propose an evolutionary a1gorithm for multi-objective optimization which uses seed individuals in order to overcome weakness of algorithms Published. Seed individual means a solution which is not located in the crowded region on Pareto front. And the idea of our algorithm uses seed individuals for reproducing individuals for next generation. Thus, proposed a1go-rithm takes advantage of local searching effect because new individuals are produced near the seed individual with high probability, and is able to produce comparatively uniform distributed pareto optimal solutions. Simulation results on five testbed problems show that the proposed algo-rithm could produce uniform distributed solutions onto pareto optimal front, and is able to show better convergence compared to NSGA-II on all testbed problems except multi-modal problem.

• Journal of Korea Water Resources Association
• /
• v.36 no.2
• /
• pp.143-152
• /
• 2003

This paper suggests the improvement of the Sumjinkang for the estimation of areal averages of heavy rainfall events based on the optimal network and three existing networks. The problem consists of minimizing an objective function which includes both the accuracy of the areal mean estimation as expressed by the Kriging variance and the economic cost of the data collection. The wellknown geostatistical variance-reduction method is used in combination with SATS which is an algorithm of minimization. At the first stage, two kinds of optimal solutions are obtained by two trade-off coefficients. One of them is a optimal solution, the other is an alternative. At the second stage, a quasi optimal network and a quasi alternative are suggested so that the existing raingages near to the selected optimal raingages are included in the two solutions instead of gages of new gages.

• Journal of the Korean Mathematical Society
• /
• v.36 no.2
• /
• pp.317-332
• /
• 1999

In this paper we deal with the optimal control problem for the semilinear functional differential equations with unbounded delays. We will also establish the regularity for solutions of the given system. By using the penalty function method we derive the optimal conditions for optimality of an admissible state-control pairs.

• Journal of the Korean Mathematical Society
• /
• v.41 no.4
• /
• pp.681-715
• /
• 2004

Mathematical formulation and numerical solutions of an optimal Dirichlet boundary control problem for the Boussinesq equations are considered. The solution of the optimal control problem is obtained by adjusting of the temperature on the boundary. We analyze finite element approximations. A gradient method for the solution of the discrete optimal control problem is presented and analyzed. Finally, the results of some computational experiments are presented.

• 제어로봇시스템학회:학술대회논문집
• /
• 1992.10a
• /
• pp.572-577
• /
• 1992

In this paper, the optimal homing guidance problem is investigated for the general missile/target models described in the state-space. The closed-form solution of the optimal guidance law derived, and its asymptotic properties are studied as the time-to-go goes to infinity or zero. Futhermore, several approximate solutions of the optimal guidance law are suggested for real-time applications.

• Journal of applied mathematics & informatics
• /
• v.6 no.1
• /
• pp.291-301
• /
• 1999

We study an optimal control problem of the fluid flow governed by the navier-Stokes equations. The control problem is formulated with the flow in the driven cavity. Existence of an optimal solution and first-order optimality condition of the optimal control are derived. We report the numerical results for the finite eleme수 approximations of the optimal solutions.

• Proceedings of the KIEE Conference
• /
• 2002.11b
• /
• pp.381-383
• /
• 2002

This paper proposes a MATLAB program for solving security-constrained optimal power flow using linear programming. Security-constrained optimal power flow can find an optimal generation satisfying bus voltage limits, line flow limits, reactive generation limits, even if contingency occurs. Sensitivity matrixes are obtained based on power flow solutions with and without single line contingency. This program is tested for an IEEE 14bus system with 5 generators Results shows good ability of finding optimal solution in case of a single line contingency.

• Management Science and Financial Engineering
• /
• v.16 no.3
• /
• pp.71-84
• /
• 2010

In this study, we present the exact methods of transforming the continuous solutions into the discrete ones for two types of bit-loading problem, marginal adaptive (MA) and rate adaptive (RA) problem, in multicarrier communication systems. While the computational complexity of existing solution methods for discrete optimal solutions depends on the number of bits to be assigned (R), the proposed method determined by the number of subcarriers (N), making ours be more efficient in most cases where R is much larger than N. Furthermore our methods have some strength of their simpler form to make a practical use.