• Structural Engineering and Mechanics
• /
• v.52 no.2
• /
• pp.405-426
• /
• 2014

In this paper, a new meta-heuristic algorithm named Ranked Particles Optimization (RPO), is presented. This algorithm is not inspired from natural or physical phenomena. However, it is based on numerous researches in the field of meta-heuristic optimization algorithms. In this algorithm, like other meta-heuristic algorithms, optimization process starts with by producing a population of random solutions, Particles, located in the feasible search space. In the next step, cost functions corresponding to all random particles are evaluated and some of those having minimum cost functions are stored. These particles are ranked and their weighted average is calculated and named Ranked Center. New solutions are produced by moving each particle along its previous motion, the ranked center, and the best particle found thus far. The robustness of this algorithm is verified by solving some mathematical and structural optimization problems. Simplicity of implementation and reaching to desired solution are two main characteristics of this algorithm.

• International Journal of Reliability and Applications
• /
• v.9 no.1
• /
• pp.1-5
• /
• 2008

A heuristic algorithm for reliability optimization of a distributed network system is developed so that the reliability of a large system can be determined efficiently. This heuristic bases on the determination of maximal reliability set of maximum node capacity, maximal link reliability and maximal node degree.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• v.1 no.1
• /
• pp.6-12
• /
• 2001

Evolutionary algorithms(EAs) have been successfully applied to many combinatorial optimization problems of various engineering fields. Recently, some comparative studies of EAs with other stochastic search algorithms have, however, shown that they are similar to, or even are not comparable to other heuristic search. In this paper, a new hybrid evolutionary algorithm utilizing a new local heuristic search, for combinatorial optimization problems, is presented. The new intelligent local heuristic search is described, and the behavior of the hybrid search algorithm is investigated on two well-known problems: traveling salesman problems (TSPs), and quadratic assignment problems(QAPs). The results indicate that the proposed hybrid is able to produce solutions of high quality compared with some of evolutionary and simulated annealing.

• Proceedings of the Korea Society for Industrial Systems Conference
• /
• 2008.10b
• /
• pp.463-467
• /
• 2008

Heuristic optimization using hybrid algorithms have provided a robust and efficient approach for solving many optimization problems. In this paper, a new hybrid algorithm using adaptive genetic algorithm (aGA) and particle swarm optimization (PSO) is proposed. The proposed hybrid algorithm is applied to solve numerical optimization functions. The results are compared with those of GA and other conventional PSOs. Finally, the proposed hybrid algorithm outperforms others.

• Journal of the Korean Society of Marine Environment & Safety
• /
• v.5 no.2
• /
• pp.87-97
• /
• 1999

This study is concerned with developing a hybrid heuristic algorithm for solving the redundancy optimization problem which is very important in system safety, This study develops a HH(Hybrid Heuristic) method combined with two strategies to alleviate the risks of being trapped at a local optimum. One of them is to construct the populations of the initial solutions randomly. The other is the additional search with SA(Simulated Annealing) method in final step. Computational results indicate that HH performs consistently better than the KY method proposed in Kim[8]. Therefore, the proposed HH is believed to an attractive to other heuristic methods.

• Structural Engineering and Mechanics
• /
• v.55 no.3
• /
• pp.535-554
• /
• 2015

Dolphin echolocation (DE) optimization algorithm is a recently developed meta-heuristic in which echolocation behavior of Dolphins is utilized for seeking a design space. The computational performance of meta-heuristic algorithms is highly dependent to its internal parameters. But the computational time of adjusting these parameters is usually extensive. The DE is an efficient optimization algorithm as it includes few internal parameters compared with other meta-heuristics. In the present paper a modified Dolphin echolocation (MDE) algorithm is proposed for optimization of steel frame structures. In the MDE the step locations are determined using one-dimensional chaotic maps and this improves the convergence behavior of the algorithm. The effectiveness of the proposed MDE algorithm is illustrated in three benchmark steel frame optimization test examples. Results demonstrate the efficiency of the proposed MDE algorithm in finding better solutions compared to standard DE and other existing algorithms.

• Proceedings of the KIEE Conference
• /
• 2008.07a
• /
• pp.303-304
• /
• 2008

For the state estimation problem, the weighted least squares (WLS) method and the fast decoupled method are widely used at present. However, these algorithms can converge to local optimal solutions. Recently, modern heuristic optimization methods such as Particle Swarm Optimization (PSO) have been introduced to overcome the disadvantage of the classical optimization problem. However, heuristic optimization methods based on populations require a lengthy computing time to find an optimal solution. In this paper, we used PSO to search for the optimal solution of state estimation in power systems. To overcome the shortcoming of heuristic optimization methods, we proposed parallel processing of the PSO algorithm based on the PC cluster system. the proposed approach was tested with the IEEE-118 bus systems. From the simulation results, we found that the parallel PSO based on the PC cluster system can be applicable for power system state estimation.

• Geomechanics and Engineering
• /
• v.12 no.2
• /
• pp.197-210
• /
• 2017

This paper focuses on the regularization of structural configurations by employing meta-heuristic optimization algorithms such as Particle Swarm Optimization (PSO) and Biogeography-Based Optimization (BBO). The regularization of structural configuration means obtaining a structure whose members have equal or almost equal lengths, or whose member's lengths are based on a specific pattern; which in this case, by changing the length of these elements and reducing the number of different profiles of needed members, the construction of the considered structure can be made easier. In this article, two different objective functions have been used to minimize the difference between member lengths with a specific pattern. It is found that by using a small number of iterations in these optimization methods, a structure made of equal-length members can be obtained.

• Structural Engineering and Mechanics
• /
• v.60 no.6
• /
• pp.1093-1117
• /
• 2016

Global sensitivity analysis (GSA) has been widely used to investigate the sensitivity of the model output with respect to its input parameters. In this paper a new single-solution search optimization algorithm is developed based on the GSA, and applied to the size optimization of truss structures. In this method the search space of the optimization is determined using the sensitivity indicator of variables. Unlike the common meta-heuristic algorithms, where all the variables are simultaneously changed in the optimization process, in this approach the sensitive variables of solution are iteratively changed more rapidly than the less sensitive ones in the search space. Comparisons of the present results with those of some previous population-based meta-heuristic algorithms demonstrate its capability, especially for decreasing the number of fitness functions evaluations, in solving the presented benchmark problems.

• The KIPS Transactions:PartB
• /
• v.17B no.5
• /
• pp.389-398
• /
• 2010

The Vehicle Routing and Scheduling Problem with Time Windows(VRSPTW) is to establish a delivery route of minimum cost satisfying the time constraints and capacity demands of many customers. The VRSPTW takes a long time to generate a solution because this is a NP-hard problem. To generate the nearest optimal solution within a reasonable time, we propose the heuristic by using an ACO(Ant Colony Optimization) with multi-cost functions. The multi-cost functions can generate a feasible initial-route by applying various weight values, such as distance, demand, angle and time window, to the cost factors when each ant evaluates the cost to move to the next customer node. Our experimental results show that our heuristic can generate the nearest optimal solution more efficiently than Solomon I1 heuristic or Hybrid heuristic applied by the opportunity time.