• 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.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.3
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• pp.117-126
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• 2016

In this study, a new meta-heuristic optimization algorithm, Vision Correction Algorithm (VCA), designed according to the optical properties of glasses was developed. The VCA is a technique applying optometry and vision correction procedure to optimization algorithm through the process of myopic/hyperopic correction-brightness adjustment-compression enforcement-astigmatism adjustment. The proposed VCA unlike the conventional meta-heuristic algorithm is an automatically adjusting global/local search rate and global search direction based on accumulated optimization results. The proposed algorithm was applied to the representative optimization problem (mathematical and engineering problem) and results of the application are compared with that of the present algorithms.

• Nuclear Engineering and Technology
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• v.53 no.11
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• pp.3772-3783
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• 2021

This work proposes a new efficient meta-heuristic optimization algorithm called Gamma Ray Interactions Based Optimization (GRIBO). The algorithm mimics different energy loss processes of a gamma-ray photon during its passage through a matter. The proposed novel algorithm has been applied to search for the global minima of 30 standard benchmark functions. The paper also considers solving real optimization problem in the field of nuclear engineering, radioisotope identification. The results are compared with those obtained by the Particle Swarm Optimization, Genetic Algorithm, Gravitational Search Algorithm and Grey Wolf Optimizer algorithms. The comparisons indicate that the GRIBO algorithm is able to provide very competitive results compared to other well-known meta-heuristics.

• Journal of Distribution Science
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• v.11 no.6
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• pp.13-19
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• 2013

Purpose - This study aims to predict the audit reports of listed companies on the Tehran Stock Exchange by using meta-heuristic algorithms. Research design, data, methodology - This applied research aims to predict auditors reports' using meta-heuristic methods (i.e., neural networks, the ANFIS, and a genetic algorithm). The sample includes all firms listed on the Tehran Stock Exchange. The research covers the seven years between 2005 and 2011. Results - The results show that the ANFIS model using fuzzy clustering and a least-squares back propagation algorithm has the best performance among the tested models, with an error rate of 4% for incorrect predictions and 96% for correct predictions. Conclusion - A decision tree was used with ten independent variables and one dependent variable the less important variables were removed, leaving only those variables with the greatest effect on auditor opinion (i.e., net-profit-to-sales ratio, current ratio, quick ratio, inventory turnover, collection period, and debt coverage ratio).

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

• Structural Engineering and Mechanics
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• v.42 no.6
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• pp.783-797
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• 2012

A new hybrid meta-heuristic optimization algorithm is presented for design of structures. The algorithm is based on the concepts of the charged system search (CSS) and the particle swarm optimization (PSO) algorithms. The CSS is inspired by the Coulomb and Gauss's laws of electrostatics in physics, the governing laws of motion from the Newtonian mechanics, and the PSO is based on the swarm intelligence and utilizes the information of the best fitness historically achieved by the particles (local best) and by the best among all the particles (global best). In the new hybrid algorithm, each agent is affected by local and global best positions stored in the charged memory considering the governing laws of electrical physics. Three different types of structures are optimized as the numerical examples with the new algorithm. Comparison of the results of the hybrid algorithm with those of other meta-heuristic algorithms proves the robustness of the new algorithm.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.7 no.1
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• pp.89-95
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• 2006

Planning distributed manufacturing logistics is one of main issues in supply chain management. This paper proposes a hybrid heuristic approach for the Multi-Level, multi-item Capacitated Lot Sizing Problem (MLCLSP) in supply chain network. MLCLSP corresponds to a mixed integer programming (MIP) problem. With integer variable solutions determined by heuristic search, this MIP problem becomes linear program (LP). By repeatedly solving the relaxed MIP problems with a heuristic search method in a hybrid manner, this proposed approach allocates finite manufacturing resources fur each distributed facilities and generates feasible production plans. Meta heuristic search algorithm is presented to solve the MIP problems. The experimental test evaluates the computational performance under a variety of problem scenarios.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2004.05a
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• pp.409-412
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• 2004

Today, meta-heuristic algorithms have been much attention by researcher because they have the power of solving combinational optimization problems efficiently. As the result, many variants of a meta-heuristic algorithm (e.g., simulated annealing) have been proposed for specific application domains. However, there are few efforts to classify them into a unified software framework, which is believed to provide the users with the reusability of the software, thereby significantly reducing the development time of algorithms. In this paper, we present an object-oriented framework to be used as a general tool for efficiently developing variants of simulated annealing algorithm. The interface classes in the framework achieve the modulization of the algorithm, and the users are allowed to specialize some of the classes appropriate for solving their problems. The core of the framework is Algorithm Configuration Pattern (ACP) which facilitates creating user-specific variants flexibly. Finally, we summarize our experiences and discuss future research topics.

• Steel and Composite Structures
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• v.16 no.6
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• pp.681-701
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• 2014

The main aim of this study is to propose an efficient meta-heuristic algorithm for topology optimization of geometrically nonlinear single layer domes by serially integration of computational advantages of firefly algorithm (FA) and particle swarm optimization (PSO). During the optimization process, the optimum number of rings, the optimum height of crown and tubular section of the member groups are determined considering geometric nonlinear behaviour of the domes. In the proposed algorithm, termed as FA-PSO, in the first stage an optimization process is accomplished using FA to explore the design space then, in the second stage, a local search is performed using PSO around the best solution found by FA. The optimum designs obtained by the proposed algorithm are compared with those reported in the literature and it is demonstrated that the FA-PSO converges to better solutions spending less computational cost emphasizing on the efficiency of the proposed algorithm.

• Journal of Electrical Engineering and Technology
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• v.12 no.4
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• pp.1417-1426
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• 2017

Power system analysis, Non-Convex Economic Dispatch (NED) is considered as an open and demanding optimization problem. Despite the fact that realistic ED problems have non-convex cost functions with equality and inequality constraints, conventional search methods have not been able to effectively find the global answers. Considering the great potential of meta-heuristic optimization techniques, many researchers have started applying these techniques in order to solve NED problems. In this paper, a new and efficient approach is proposed based on imperialist competitive algorithm (ICA). The proposed algorithm which is named multi-operator ICA (MuICA) merges three operators with the original ICA in order to simultaneously avoid the premature convergence and achieve the global optimum answer. In this study, the proposed algorithm has been applied to different test systems and the results have been compared with other optimization methods, tending to study the performance of the MuICA. Simulation results are the confirmation of superior performance of MuICA in solving NED problems.