• Industrial Engineering and Management Systems
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• v.11 no.3
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• pp.215-223
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• 2012

This paper focuses on three very similar evolutionary algorithms: genetic algorithm (GA), particle swarm optimization (PSO), and differential evolution (DE). While GA is more suitable for discrete optimization, PSO and DE are more natural for continuous optimization. The paper first gives a brief introduction to the three EA techniques to highlight the common computational procedures. The general observations on the similarities and differences among the three algorithms based on computational steps are discussed, contrasting the basic performances of algorithms. Summary of relevant literatures is given on job shop, flexible job shop, vehicle routing, location-allocation, and multimode resource constrained project scheduling problems.

• Journal of Communications and Networks
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• v.13 no.4
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• pp.327-338
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• 2011

With the incitation to reduce power consumption and the aggressive reuse of spectral resources, there is an inevitable trend towards the deployment of small-cell networks by decomposing a traditional single-tier network into a multi-tier network with very high throughput per network area. However, this cell size reduction increases the complexity of network operation and the severity of cross-tier interference. In this paper, we consider a downlink two-tier network comprising of a multiple-antenna macrocell base station and a single femtocell access point, each serving multiples users with a single antenna. In this scenario, we treat the following beamforming optimization problems: i) Total transmit power minimization problem; ii) mean-square error balancing problem; and iii) interference power minimization problem. In the presence of perfect channel state information (CSI), we formulate the optimization algorithms in a centralized manner and determine the optimal beamformers using standard convex optimization techniques. In addition, we propose semi-decentralized algorithms to overcome the drawback of centralized design by introducing the signal-to-leakage plus noise ratio criteria. Taking into account imperfect CSI for both centralized and semi-decentralized approaches, we also propose robust algorithms tailored by the worst-case design to mitigate the effect of channel uncertainty. Finally, numerical results are presented to validate our proposed algorithms.

• International Journal of Computer Science & Network Security
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• v.21 no.4
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• pp.33-40
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• 2021

The traveling salesman problem (TSP) is one of the well-known and extensively studied NPC problems in combinatorial optimization. To solve it effectively and efficiently, various optimization algorithms have been developed by scientists and researchers. However, most optimization algorithms are designed based on the concept of improving route in the iterative improvement process so that the optimal solution can be finally found. In contrast, there have been relatively few algorithms to find the optimal solution using route construction mechanism. In this paper, we propose a route construction optimization algorithm to solve the symmetric TSP with the help of ratio value. The proposed algorithm starts with a set of sub-routes consisting of three cities, and then each good sub-route is enhanced step by step on both ends until feasible routes are formed. Before each subsequent expansion, a ratio value is adopted such that the good routes are retained. The experiments are conducted on a collection of benchmark symmetric TSP datasets to evaluate the algorithm. The experimental results demonstrate that the proposed algorithm produces the best-known optimal results in some cases, and performs better than some other route construction optimization algorithms in many symmetric TSP datasets.

• Geomechanics and Engineering
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• v.24 no.3
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• pp.237-251
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• 2021

The main purpose of this study is to investigate the performance of the proposed hybrid teaching-learning based optimization algorithm on the optimum design of reinforced concrete (RC) cantilever retaining walls. For this purpose, three different design examples are optimized with 100 independent runs considering continuous and discrete variables. In order to determine the algorithm performance, the optimization results were compared with the outcomes of the nine powerful meta-heuristic algorithms applied to this problem, previously: the big bang-big crunch (BB-BC), the biogeography based optimization (BBO), the flower pollination (FPA), the grey wolf optimization (GWO), the harmony search (HS), the particle swarm optimization (PSO), the teaching-learning based optimization (TLBO), the jaya (JA), and Rao-3 algorithms. Moreover, Rao-1 and Rao-2 algorithms are applied to this design problem for the first time. The objective function is defined as minimizing the total material and labor costs including concrete, steel, and formwork per unit length of the cantilever retaining walls subjected to the requirements of the American Concrete Institute (ACI 318-05). Furthermore, the effects of peak ground acceleration value on minimum total cost is investigated using various stem height, surcharge loads, and backfill slope angle. Finally, the most robust results were obtained by HTLBO with 50 populations. Consequently the optimization results show that, depending on the increase in PGA value, the optimum cost of RC cantilever retaining walls increases smoothly with the stem height but increases rapidly with the surcharge loads and backfill slope angle.

• Journal of Korean Institute of Industrial Engineers
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• v.37 no.4
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• pp.408-414
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• 2011

The objective of this paper is to define local optimization algorithms (LOA) to solve Resource-Constrained Project Scheduling Problem (RCPSP) and analyze the performance of these algorithms. By representing solutions with activity list, three primitive LOAs, i.e. forward and backward improvement-based, exchange-based, and relocation-based LOAs are defined. Also, combined LOAs integrating two primitive LOAs are developed. From the experiments with standard test set J120 generated using ProGen, the FBI-based LOA demonstrates to be an efficient algorithm. Moreover, algorithms combined with FBI-based LOA and other LOA generate good solutions in general. Among the considered algorithms, the combined algorithm of FBI-based and exchangebased shows best performance in terms of solution quality and computation time.

• Proceedings of the KSME Conference
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• 2000.04a
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• pp.725-730
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• 2000

Genetic algorithms based on the theory of natural selection, have been applied to many different fields, and have proven to be relatively robust means to search for global optimum and handle discontinuous or even discrete data. Genetic algorithms are widely used for unconstrained optimization problems. However, their application to constrained optimization problems remains unsettled. The most prevalent technique for coping with infeasible solutions is to penalize a population member for constraint violation. But, the weighting of a penalty for a particular problem constraint is usually determined in the heuristic way. Therefore this paper proposes, the effective technique for handling constraints, the ranking penalty method and hybrid genetic algorithms. And this paper proposes dynamic mutation tate to maintain the diversity in population. The effectiveness of the proposed algorithm is tested on several test problems and results are discussed.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.25 no.4
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• pp.671-684
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• 2001

Several design problems of composite structures are studied via a global optimizer based on attraction regions. MSC/NASTRAN is adopted for static and eigenvalue analysis. The method of modified feasible direction in DOT is used for local optimization. Through the review of global optimization algorithms, the triangular patch algorithm is selected because the algorithm is known to be efficient, robust and powerful for general nonlinear optimization problems. For general applicability, various mechanical properties are considered as design objectives; strain energy, eigenvalue, weight, displacement, and buckling load. In all cases considered, the triangular patch algorithm results in a lot of optimum points and useful design patterns, that are not easy by local algorithms or conventional global algorithms can be determined.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2002.10a
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• pp.203-210
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• 2002

The great advantages on the Genetic Algorithms(GAs) are ease of implementation, and robustness in solving a wide variety of problems, several GAs based optimization models for solving complex structural problems were proposed. However, there are two major disadvantages in GAs. The first disadvantage, implementation of GAs-based optimization is computationally too expensive for practical use in the field of structural optimization, particularly for large-scale problems. The second problem is too difficult to find proper parameter for particular problem. Therefore, in this paper, a Distributed Hybrid Genetic Algorithms(DHGAs) is developed for structural optimization on a cluster of personal computers. The algorithm is applied to the minimum weight design of steel structures.

• Journal of Information Processing Systems
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• v.18 no.5
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• pp.665-676
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• 2022

In modern logistics, the effective use of the vehicle volume and loading capacity will reduce the logistic cost. Many heuristic algorithms can solve this knapsack problem, but lots of these algorithms have a drawback, that is, they often fall into locally optimal solutions. A fusion optimization method based on simulated annealing algorithm (SA) and binary particle swarm optimization algorithm (BPSO) is proposed in the paper. We establish a logistics knapsack model of the fusion optimization algorithm. Then, a new model of express logistics simulation system is used for comparing three algorithms. The experiment verifies the effectiveness of the algorithm proposed in this paper. The experimental results show that the use of BPSO-SA algorithm can improve the utilization rate and the load rate of logistics distribution vehicles. So, the number of vehicles used for distribution and the average driving distance will be reduced. The purposes of the logistics knapsack problem optimization are achieved.

• Journal of Electrical Engineering and Technology
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• v.4 no.4
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• pp.467-475
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• 2009

Congestion management is one of the technical challenges in power system deregulation. This paper presents single objective and multi-objective optimization approaches for optimal choice, location and size of Static Var Compensators (SVC) and Thyristor Controlled Series Capacitors (TCSC) in deregulated power system to improve branch loading (minimize congestion), improve voltage stability and reduce line losses. Though FACTS controllers offer many advantages, their installation cost is very high. Hence Independent System Operator (ISO) has to locate them optimally to satisfy a desired objective. This paper presents optimal location of FACTS controllers considering branch loading (BL), voltage stability (VS) and loss minimization (LM) as objectives at once using GA. It is observed that the locations that are most favorable with respect to one objective are not suitable locations with respect to other two objectives. Later these competing objectives are optimized simultaneously considering two and three objectives at a time using multi-objective Strength Pareto Evolutionary Algorithms (SPEA). The developed algorithms are tested on IEEE 30 bus system. Various cases like i) uniform line loading ii) line outage iii) bilateral and multilateral transactions between source and sink nodes have been considered to create congestion in the system. The developed algorithms show effective locations for all the cases considered for both single and multiobjective optimization studies.