Informatics Publishing Limited

Nitesh M. Sureja ¹, Ved Vyas Dwivedi ², Sanjeev Kumar ³

Affiliations
1 NIMS Institute of Engineering and Technology, NIMS University, Jaipur, Rajasthan, IN
2 Noble Group of Institutions, Junagadh, Gujarat, IN
3 Department of Information Technology, NIMS Institute of Engineering and Technology, NIMS University, Jaipur, Rajasthan, IN

Abstract

The Memetic algorithms (MA) are computational models inspired by the Cultural Revolution. Robustness, scalability and simplicity are the advantages of Memetic algorithms. By looking at this strength of MA, they are very much suitable for solving some combinatorial optimization problems which falls in the category of NP-hard problems. In this paper, we propose MA based optimization model for Dynamic Traveling Salesman Problem (DTSP), to achieve overall higher system performance and better throughput.

During implementation, results are obtained with number of randomly generated Traveling Salesman Problem instances, measured in terms of required time, quality and path length. It has been observed that the proposed model works well in terms of quality and path length. As it uses Simulated Annealing, it is found little bit less effective in terms of time.

Keywords

Memetic Algorithms, Simulated Annealing, Dynamic Traveling Salesman Problem (DTSP), Optimization Problems.

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Nitesh M. Sureja ¹, Ved Vyas Dwivedi ², Sanjeev Kumar ³

Affiliations
1 NIMS Institute of Engineering and Technology, NIMS University, Jaipur, Rajasthan, IN
2 Noble Group of Institutions, Junagadh, Gujarat, IN
3 Department of Information Technology, NIMS Institute of Engineering and Technology, NIMS University, Jaipur, Rajasthan, IN

Abstract

Ant Colony Optimization (ACO) is a novel and competitive optimization method for numerous combinatorial optimization problems. It is already applied to various optimization problems. Normally it proved best in terms of solution quality, accuracy and other parameters. This paper presents an Elitist Ant System (EAS) which uses tuning in pheromone update process to improve the performance of the basic Ant System (AS) approach. Dynamic Traveling Salesman Problem is solved in this research with the various pheromone update strategy for finding the improvements in the results. The results obtained are empirically compared with the results obtained with the basic pheromone update strategy of Ant System.

Keywords

Ant Colony Optimization, ElitistAnt System, Dynamic Traveling Salesman Problem (DTSP), Optimization.

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Nitesh M. Sureja ¹, Ved Vyas Dwivedi ², Sanjeev Kumar ³

Affiliations
1 NIMS Institute of Engineering and Technology, Nims University, Jaipur, Rajasthan, IN
2 Noble Group of Institutions, Junagadh, Gujarat, IN
3 Department of Information Technology, NIMS Institute of Engineering and Technology, Nims University, Jaipur, Rajasthan, IN

Abstract

Cuckoo Search is a recently developed, novel and competitive Nature Inspired Optimization algorithm used for solving various linear and discrete combinatorial optimization problems. It is already applied to the problems like PCB Drilling, finding optimal features, optimizing the parameters of various classifiers including Neural Network, RBF, SVM parameters, finding optimizing cluster centers, job scheduling, find optimal path and many more in the different domains including industry, health sector, wireless sensor network, image processing and others. Due to its proven capacity in solution quality, accuracy and other parameters, in this paper we present a cuckoo search based model to solve Dynamic Traveling Salesman Problem. The results obtained are compared with the results obtained with the other models proposed for the same problem. Results obtained are very good in terms of all related parameters.

Keywords

Cuckoo Search Algorithm, Combinatorial Optimization, Dynamic Traveling Salesman Problem, Nature Inspired Algorithms, Random Walk.

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Nitesh M. Sureja ¹, Ved Vyas Dwivedi ²

Affiliations
1 NIMS University, Jaipur, Rajasthan, IN
2 Noble Group of Institutions, Junagadh, Gujarat, IN

Abstract

There are varieties of problems in various engineering branches which can’t be solved using traditional search techniques. These problems are known as NP hard problems. The problem which can’t be solved in a polynomial time is called a NP hard problem. This type of problem normally involved exhaustive searching in side it. Various Nature inspired techniques are developed and employed by the researchers to solve this kind of problems. Evolutionary algorithms (EAs) are one of them. These algorithms are known as optimization algorithms as they give very near optimal solution. In this paper, we investigate the ability of Evolutionary Algorithm (EA) by applying it to Dynamic Traveling Salesman Problem (DTSP). Dynamic Traveling Salesman Problem is a real world TSP where problem changes itself over the period of the time. It is very difficult to solve it by using traditional methods. We apply an optimization method, evolutionary algorithm to it and obtain the results. Experimental results indicate that EAs can overcome many problems encountered by traditional search techniques. The performance of EAs is compared to the results of traditional search techniques.

Keywords

Nature Inspired Algorithm, Evolutionary Algorithms, Optimization, Dynamic Traveling Salesman Problem.

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