Application Of Metaheuristic Algorithms Research Articles

Application of metaheuristic algorithms in optimum thermal design analysis of a rectangular porous fin subjected to both insulated and convective tip conditions

In this novel optimization analysis, a rectangular porous fin subjected to convective tip conditions along with insulated end condition is studied. The aim is to optimize the important variables responsible for transferring heat from fin with insulated as well as convective tip to the surrounding in order to get a higher rate of heat transfer from the fin surface. Temperature-dependent solid and fluid thermal conductivities as well as variable internal heat generation is considered. To obtain heat dissipation rate through porous fin, the nonlinear governing equation is first solved analytically using Adomian decomposition method. Then the heat transfer rate, i.e., objective function of this problem is optimized with three powerful metaheuristic techniques, namely firefly algorithm, particle swarm optimization and gravitational search algorithm. A comparative analysis has revealed that fins with convective end show significantly higher heat transfer rate compared to their insulated end counterparts. Particle swarm optimization showed marginally higher heat transfer values compared to firefly algorithm but firefly algorithm took less computational effort to reach the optimum value.

A Comprehensive Review of approaches used for solving Tele-center location allocation problem in geographical plane

Setup of tele-centre is the worldwide approach for the establishment of information and communication technology infrastructure in rural areas for the overall development of a country. It is a key resource under e-governance plan in any country, but a major problem with their location allocation is the sustainability. Tele-centre establishment require a suitable location to increase the beneficial effect to service seekers. In this research, multi-faceted problems faced by tele-centres are highlighted. This paper presents a comprehensive study on tele-centre's location allocation problem and all the recent development in multi-facility location problem research area through more than 150 research papers from high ranked peer-reviewed journals. The research survey examines all the important parameters for the facility location problem and an objective function is also formulated for the same. Based on the survey literature, it is found that the new allocation methods based on meta-heuristic algorithms are emerging. This study would be a useful contribution in the field of location science, tele-centre location allocation and application of meta-heuristic algorithms in e-governance.

Water allocation from Hirakud Dam, Odisha, India for irrigation and power generation using optimization techniques

ABSTRACT The current research presents a case study of optimal allocation of water for irrigation and power generation at Hirakud reservoir with the application of meta-heuristic algorithm. Hirakud is a multipurpose project, which is situated in the Sambalpur district of Odisha, India. As the inflow to the reservoir is uncertain, it creates difficulties and complexities in dealing with the reservoir operation. In this work, an effort has been made to build a policy for reservoir operation in Hirakud multipurpose reservoir with a view to satisfying the aim of irrigation and hydropower generation. The conventional methods are not suitable to solve these types of problems involving complexity and nonlinearity in demand and supply. The advantages of using evolutionary algorithm for reservoir optimization are that they deal with objective function information and avoid complications associated with determining derivate or other auxiliary information. The optimization techniques like genetic algorithms, particle swarm optimization, ant colony optimization, etc., have been applied to find optimal release for irrigation and hydropower generation. The analysis indicates performance of ACO a meta-heuristics-based algorithm that provides better result as compared to other methods.

Kafes Sistemlerin BB-BC Algoritması ile Boyut Optimizasyonu Üzerine bir Çalışma: İnceleme ve Sayısal Deneyler

In the last decades, great attention has been paid to structural optimization with stochastic methods. The applications of metaheuristic algorithms have become popular, which mostly provide solutions with adequate precision for the structural optimization problems from an engineering point of view. However, these algorithms should be specifically tuned for the considered problem to obtain satisfactory results. Big Bang - Big Crunch is one of the efficient metaheuristic optimization algorithms that is based on the famous theory on the evolution of the universe. A considerable number of researchers presented applications of the Big Bang - Big Crunch algorithm for the size optimization of trusses, which is an inviting challenge in the structural optimization field. This study revisits the size optimization of trusses with continuous variables using the Big Bang - Big Crunch algorithm, discusses the previously introduced improvements and presents the results of a few experimental modifications.

Investigating the application of meta-heuristic algorithms in optimising opportunistic maintenance: an exploratory study

Investigating the application of meta-heuristic algorithms in optimising opportunistic maintenance: an exploratory study

Investigating the application of meta-heuristic algorithms in optimising opportunistic maintenance: an exploratory study

Opportunistic maintenance is very effective in optimising the existing costs of production. The aim of this article is to optimise opportunistic maintenance. For this purpose, based on an example, various methods such as genetic algorithm, imperialist competition algorithm, simulated annealing algorithm, artificial bee colony algorithm and particle swarm algorithm have been considered for solving the problem and the approaches have been compared to find the most cost effective approach. Findings imply that the imperialist competition algorithm is the most suitable solution.

Performance prediction of circular saw machine using imperialist competitive algorithm and fuzzy clustering technique

The purpose of this study is the application of meta-heuristic algorithms and fuzzy logic in the optimization and clustering to predict the sawability of dimension stone. Survey and classification of dimension stones based on their physical and mechanical properties can be so impressive in the optimization of machine applications that are in this industry such as circular diamond saw block cutting machine. In this paper, physical and mechanical properties were obtained from laboratory testing on dimension stone block samples collected from 12 quarries located in Iran and their results were optimized and classified by one of the strongest meta-heuristic algorithms and fuzzy clustering technique. The clustering of dimension stone was determined by Lloyd’s algorithm (k-means clustering) based on imperialist competitive algorithm and fuzzy C-mean by MATLAB software. The hourly production rate of each studied dimension stones was considered as a criterion to evaluate the clustering efficacy. The results of this study showed that the Imperialist Competitive algorithm and fuzzy C-mean are very suitable for clustering with respect to the physical and mechanical properties of the dimension stone, and the results obtained showed the superiority of the ICA.

Recent advances on the use of meta-heuristic optimization algorithms to optimize the type-2 fuzzy logic systems in intelligent control

Finding the appropriate values of parameters and structure of type-2 fuzzy logic systems is a difficult and complex task. Many types of meta-heuristic algorithms have been used to find the complex structure and appropriate parameter values of the type-2 fuzzy systems and more recently hybrid meta-heuristic algorithms. In this paper, we review recent advances (2012 to date) on the application of meta-heuristic algorithms and hybrid meta-heuristic algorithms, for the optimization of type-2 fuzzy logic systems in intelligent control. It was found that the major meta-heuristic algorithms used for optimizing the design of type-2 fuzzy logic systems in intelligent control were genetic algorithms and particle swarm optimization as well as hybrid meta-heuristic algorithms. Researchers can use this review as a starting point for further advancement as well as an exploration of other meta-heuristic algorithms that have received little or no attention from researchers.

Application of meta-heuristic algorithms for automated software test data generation

The quality of a developed software system depends on factors such as reliability, maintainability, scalability etc. To deliver reliable software, it is necessary that it is defect free. In order to achieve this objective, the software system needs to be thoroughly tested. Since exhaustive testing is not possible, it is necessary to identify suitable test data that helps in improving the quality of a software product. Manual generation of test data is laborious and time consuming due to the presence of a huge number of decision nodes in a program. Meta-heuristic algorithms can help in such scenario by generating optimal test data automatically, from a very large search space of candidate solutions. In this paper, an attempt is made to automatically generate test data using five meta-heuristic algorithms. The proposed approach is applied for a case study bank ATM. Results show that meta-heuristics are efficient in generating optimal test data.

Application of Particle Swarm Optimization to Microwave Tapered Microstrip Lines

Application of metaheuristic algorithms has been of continued interest in the field of electrical engineering because of their powerful features. In this work special design is done for a tapered transmission line used for matching an arbitrary real load to a 50{\Omega} line. The problem at hand is to match this arbitrary load to 50 {\Omega} line using three section tapered transmission line with impedances in decreasing order from the load. So the problem becomes optimizing an equation with three unknowns with various conditions. The optimized values are obtained using Particle Swarm Optimization. It can easily be shown that PSO is very strong in solving this kind of multiobjective optimization problems.

APPLICATION OF PARTICLE SWARM OPTIMIZATION TO MICROWAVE TAPERED MICROSTRIP LINES

Application of metaheuristic algorithms has been of continued interest in the field of electrical engineering because of their powerful features. In this work special design is done for a tapered transmission line used for matching an arbitrary real load to a 50Ω line. The problem at hand is to match this arbitray load to 50 Ω line using three section tapered transmission line with impedances in decreasing order from the load. So the problem becomes optimizing an equation with three unknowns with various conditions. The optimized values are obtained using Particle Swarm Optimi zation. It can easily be shown that PSO is very strong in solving this kind of multiobjective optimization problems .

SCHEDULING OF CONSTRUCTION PROJECTS WITH APPLICATION OF METAHEURISTIC ALGORITHMS

The paper presents the authors’ research on the application of metaheuristic algorithms in Time Coupling Methods (TCM). The experimental analysis of algorithms: tabu search, genetic search, simulated annealing and B&B algorithm was conducted in the paper. The application of these algorithms, which are currently used to solve job scheduling problems, allows one to obtain better suboptimal solutions than with the currently used B&B algorithm. The main branch of the authors’ research is developing the methodology of construction works scheduling with the application of TCM 17,8,9 1013. The problems of scheduling linear construction works using time-cost optimisation are worked out.

A comparison of two chromosome representation schemes used in solving a family-based scheduling problem

Meta-heuristic algorithms have been widely used in solving scheduling problems; previous studies focused on enhancing existing algorithmic mechanisms. This study advocates a new perspective-developing new chromosome (solution) representation schemes may improve the performance of existing meta-heuristic algorithms. In the context of a scheduling problem, known as permutation manufacturing-cell flow shop (PMFS), we compare the effectiveness of two chromosome representation schemes (Sold and Snew) while they are embedded in a meta-heuristic algorithm to solve the PMFS scheduling problem. Two existing meta-heuristic algorithms, genetic algorithm (GA) and ant colony optimization (ACO), are tested. Denote a tested meta-heuristic algorithm by X_Y, where X represents an algorithmic mechanism and Y represents a chromosome representation. Experiment results indicate that GA_ Snew outperforms GA_Sold, and ACO_Snew also outperforms ACO_Sold. These findings reveal the importance of developing new chromosome representations in the application of meta-heuristic algorithms.

A Review of the Application ofMeta-Heuristic Algorithms to 2D Strip Packing Problems

This paper is a review of the approaches developed to solve 2D packing problems with meta-heuristic algorithms. As packing tasks are combinatorial problems with very large search spaces, the recent literature encourages the use of meta-heuristic search methods, in particular genetic algorithms. The objective of this paper is to present and categorise the solution approaches in the literature for 2D regular and irregular strip packing problems. The focus is hereby on the analysis of the methods involving genetic algorithms. An overview of the methods applying other meta-heuristic algorithms including simulated annealing, tabu search, and artificial neural networks is also given.