Prey Predator Algorithm for Travelling Salesman Problem

Abstract

Visiting most, if not all, tourist destination of a country while visiting a country is an ideal plan of a tourist. In most cases if the tour is not carefully planned, it will be costly and time taking to travel between tourist destinations of a country. If we consider Ethiopia, a country which has been named as best tourism destination for 2015 by the European Council on Tourism and Trade (ECTT); there are many tourist destinations all over the country. The problem of determining the optimum route to visit all the tourist sites with minimum traveling time can be formulated as a travel salesman problem. In this study 17 of the famous tourist destination of Ethiopia will be selected and a travel salesman model will be formulated. Due to the NP hardness of the travel salesman problem, metaheuristic based algorithms are found to be more effective. Hence, a recently introduced swarm based metaheuristic algorithm, called prey predator algorithm will be used to deal with the formulated problem. The simulation result suggests that the best route to visit the selected destination is Addis Ababa ? Sof Omar Caves ? Bale Mountain National Park ? Abijiata-Shalla Lakes ? Netchisar National Park ? Mago National Park ? Omo National Park ? Gambella National Park ? Bahir Dar ? Lalibela ? Gonder ? Semien Mountain National Park ? Axum ? Ertale ? Yangudi Rassa National Park ? Awash National Park ? Harar ? Addis Ababa. It should be noted in some of the sites there is no direct route and hence a route through other cities is used and hence it should be recomputed in the future when a direct route between these tourist destinations is constructed.

Prediction of thermal conductivity of polyvinylpyrrolidone (PVP) electrospun nanocomposite fibers using artificial neural network and prey-predator algorithm

“…Prey predator algorithm is one of the new metaheuristic algorithms for optimization problems [ 17 ]. It has better exploration properties compared to other algorithms, such as particle swarm optimization algorithm and genetic algorithm.…”

“…The best prey in the other hand does only a local search for exploitation purpose. The main steps of the prey predictor algorithm for training experimental data are as follows (see Fig 5 ) [ 16 , 17 ].…”

“…PPA is a new metaheuristic algorithm, introduced by Tilahun and Ong [ 16 ]. It is inspired by the interaction between a predator and prey of animals in the ecosystem [ 17 , 18 ]. PPA Predicted thermal conductivity values obtained from network were examined statistically and compared with actual values obtained from experiments.…”

Designing with biodiversity data: connections among design, materials, and technology

“…In comparison, venues such as museums are recognised as public spaces where the functions of collection, research, exhibition and recreation are served [23]; they are especially important as sites for educational purposes as well, where individuals with manifold backgrounds can receive educational information. However, some deem that the museum audience is rather diverse considering its nature as a tourist attraction for short and mainly non-repeating tourists [2,24]. Some museum studies reveal the way that tourists experience interactive exploration in the museum and how those interactivities can be further augmented [3,19].…”

Optimization of Microchannel Heat Sinks Using Prey-Predator Algorithm and Artificial Neural Networks

“…The algorithm has been found to be effective when applied to different problems, including radial basis function neural networks (RBFNNs), weight minimization of a speed reducer, a bi-level problem model of an electricity market, parameter setting of a grinding process, and single-frequency bus timetabling [19][20][21][22]. It has also been modified to suit combinatorial optimization problems, like the travel salesman problem, both the standard and clustered traveling salesman problems, and exam timetabling problem [23]. Furthermore, to boost its performance it has been extended to different versions [24].…”