• Steel and Composite Structures
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• v.45 no.4
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• pp.579-590
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• 2022

One of the biggest problems in structural steel calculation is the design of structures using the lowest possible material weight, making this a slow and costly process. To achieve this objective, several optimization methods have been developed and tested. Nevertheless, a method that performs very efficiently when applied to different problems is not yet available. Based on this assumption, this work proposes a hybrid metaheuristic algorithm for geometric and dimensional optimization of space trusses, called Simulated Squirrel Search Algorithm, which consists of an association of the well-established neighborhood shifting algorithm (Simulated Annealing) with a recently developed promising population algorithm (Squirrel Search Algorithm, or SSA). In this study, two models are tried, being respectively, a classical model from the literature (25-bar space truss) and a roof system composed of space trusses. The structures are subjected to resistance and displacement constraints. A penalty function using Fuzzy Logic (FL) is investigated. Comparative analyses are performed between the Squirrel Search Algorithm (SSSA) and other optimization methods present in the literature. The results obtained indicate that the proposed method can be competitive with other heuristics.

• The Journal of Information Technology and Database
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• v.7 no.2
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• pp.47-59
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• 2000

In this paper, we analyze the applicability of a general optimization algorithm to a database optimization problem. The index selection problem Is the problem to choose a set of indexes for a database in a way that the cost to process queries in the given workload is minimized subject to a given storage space restriction for storing indexes. The problem is well known in database research fields, and many optimization and/or heuristic algorithms have been proposed. Our work differs from previous research in that we formalize the problem in the form of non-linear Integer Programming model, and investigate the feasibility and applicability of a general purpose optimization algorithm, called OPBDP, through experiments. We implemented algorithms to generate workload data sets and problem instances for the experiment. The OPBDP algorithm, which is a non-linear 0-1 Integer Programming problem solver based on Davis-Putnam method, worked generally well for our problem formulation. The experiment result showed various performance characteristics depending on the types of decision variables, variable navigation methods and ocher algorithm parameters, and indicates the need of further study on the exploitation of the general purpose optimization techniques for the optimization problems in database area.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.67 no.3
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• pp.143-148
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• 2018

In this paper, the optimal fuzzy logic controller(FLC) for a hybrid renewable energy system(HRES) is proposed. Generally, hybrid renewable energy systems can consist of wind power, solar power, fuel cells and storage devices. The proposed FLC can effectively control the entire HRES by determining the output power of the fuel cell or the absorption power of the electrolyzer. In general, fuzzy logic controllers can be optimized by classical optimization algorithms such as genetic algorithms(GA) or particle swarm optimization(PSO). However, these FLC have a disadvantage in that their performance varies greatly depending on the control parameters of the optimization algorithms. Therefore, we propose a method to optimize the fuzzy logic controller using the teaching-learning based optimization(TLBO) algorithm which does not have the control parameters of the algorithm. The TLBO algorithm is an optimization algorithm that mimics the knowledge transfer mechanism in a class. To verify the performance of the proposed algorithm, we modeled the hybrid system using Matlab Tool and compare and analyze the performance with other classical optimization algorithms. The simulation results show that the proposed method shows better performance than the other methods.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.4
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• pp.1568-1589
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• 2021

Deployment of access point (AP) is a problem that must be considered in network planning. However, this problem is usually a NP-hard problem which is difficult to directly reach optimal solution. Thus, improved AP deployment optimization scheme based on swarm intelligence algorithm is proposed to research on this problem. First, the scheme estimates the number of APs. Second, the multi-objective particle swarm optimization (MOPSO) algorithm is used to optimize the location and transmit power of APs. Finally, the greedy algorithm is used to remove the redundant APs. Comparing with multi-objective whale swarm optimization algorithm (MOWOA), particle swarm optimization (PSO) and grey wolf optimization (GWO), the proposed deployment scheme can reduce AP's transmit power and improves energy efficiency under different numbers of users. From the experimental results, the proposed deployment scheme can reduce transmit power about 2%-7% and increase energy efficiency about 2%-25%, comparing with MOWOA. In addition, the proposed deployment scheme can reduce transmit power at most 50% and increase energy efficiency at most 200%, comparing with PSO and GWO.

• Advances in Computational Design
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• v.7 no.4
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• pp.297-319
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• 2022

Due to their natural and social revelation, also their ease and flexibility, human collective behavior and teamwork sports are inspired to introduce optimization algorithms to solve various engineering and scientific problems. Nowadays, meta-heuristic algorithms are becoming some striking methods for solving complex real-world problems. In that respect in the present study, the authors propose a novel meta-innovative algorithm based on soccer teamwork sport, suitable for optimization problems. The method may be referred to as the Soccer League Optimization-based Championship Algorithm, inspired by the Soccer league. This method consists of two main steps, including: 1. Qualifying competitions and 2. Main competitions. To evaluate the robustness of the proposed method, six different benchmark mathematical functions, and two engineering design problem was performed for optimization to assess its efficiency in achieving optimal solutions to various problems. The results show that the proposed algorithm may well explore better performance than some well-known algorithms in various aspects such as consistency through runs and a fast and steep convergence in all problems towards the global optimal fitness value.

• 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.

• Nuclear Engineering and Technology
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• v.56 no.8
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• pp.3425-3434
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• 2024

The main features of the loading pattern optimization (LPO) problem, such as high-dimensionality, multi-modality, and non-linearity, make it difficult to achieve a truly optimal configuration. In recent years, metaheuristic methods have been successfully used to solve this problem. In this research, a discrete golden eagle optimization (DGEO) algorithm has been developed to solve the LPO problem in the first cycle of VVER-1000 reactor core. To evaluate the proposed algorithm, a linear multi-purpose fitness function has been used to improve the safety parameters of the reactor core by obtaining a flatter power distribution during the first cycle, and also to enhance the economic parameters by increasing the cycle length and reducing the cost of fuel recycling. For this purpose, a FORTRAN program has been written to map the DGEO algorithm for the LPO problem using the PMAX and PARCS core calculation code to compute the fitness function in each iteration. To speed up the calculations, parallel computing has been applied in the written program. The results demonstrated that the loading pattern, which is suggested by the DGEO algorithm, enhances all the safety and economic parameters in the fitness function. Thus, the DGEO algorithm is highly reliable for the LPO problems in the VVER 1000 reactor core.

• Journal of Information Processing Systems
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• v.18 no.6
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• pp.719-728
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• 2022

The prediction of short-term traffic flow is the theoretical basis of intelligent transportation as well as the key technology in traffic flow induction systems. The research on short-term traffic flow prediction has showed the considerable social value. At present, the support vector regression (SVR) intelligent prediction model that is suitable for small samples has been applied in this domain. Aiming at parameter selection difficulty and prediction accuracy improvement, the artificial bee colony (ABC) is adopted in optimizing SVR parameters, which is referred to as the ABC-SVR algorithm in the paper. The simulation experiments are carried out by comparing the ABC-SVR algorithm with SVR algorithm, and the feasibility of the proposed ABC-SVR algorithm is verified by result analysis. Continuously, the simulation experiments are carried out by comparing the ABC-SVR algorithm with particle swarm optimization SVR (PSO-SVR) algorithm and genetic optimization SVR (GA-SVR) algorithm, and a better optimization effect has been attained by simulation experiments and verified by statistical test. Simultaneously, the simulation experiments are carried out by comparing the ABC-SVR algorithm and wavelet neural network time series (WNN-TS) algorithm, and the prediction accuracy of the proposed ABC-SVR algorithm is improved and satisfactory prediction effects have been obtained.

• Structural Engineering and Mechanics
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• v.7 no.5
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• pp.485-502
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• 1999

Using the continuous Hopfield network model as the basis to solve the general crisp and fuzzy constrained optimization problem is presented and examined. The model lies in its transformation to a parallel algorithm which distributes the work of numerical optimization to several simultaneously computing processors. The method is applied to different structural engineering design problems that demonstrate this usefulness, satisfaction or potential. The computing algorithm has been given and discussed for a designer who can program it without difficulty.

• Journal of the Korea Institute of Building Construction
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• v.8 no.6
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• pp.91-98
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• 2008

It is time-consuming and difficulty to solve the time-cost trade-off problems, as there are trade-offs between time and cost to complete the activities in construction projects and this problems do not have unique solutions. Typically, heuristic methods, mathematical models and GA models has been used to solve this problems. As heuristic methods and mathematical models are have weakness in solving the time-cost trade-off problems, GA based model has been studied widely in recent. This paper suggests the time-cost trade-off optimization algorithm using particle swarm optimization. The traditional particle swarm optimization model is modified to generate optimal tradeoffs among construction time and cost efficiently. An application example is analyzed to illustrate the use of the suggested algorithm and demonstrate its capabilities in generating optimal tradeoffs among construction time and cost. Future applications of the model are suggested in the conclusion.