A NOVEL METAHEURISTIC ALGORITHM: DYNAMIC VIRTUAL BATS ALGORITHM FOR GLOBAL OPTIMIZATION

A novel nature-inspired algorithm called the Dynamic Virtual Bats Algorithm (DVBA) is presented in this thesis. DVBA is inspired by a bat’s ability to manipulate frequency and wavelength of the emitted sound waves when hunting. A role based search has been developed to improve the diversification and intensification capability of standard Bat Algorithm (BA). Although DVBA is inspired from bats, like BA, it is conceptually very different from BA. BA needs a huge number of population size; however, DVBA employs just two bats to handle the ”exploration and exploitation” conflict which is known as a real challenge for all optimization algorithms. Firstly, we study bat’s echolocation ability and next, the most known bat-inspired algorithm and its modified versions are analyzed. The contributions of this thesis start reading and imitating bat’s hunting strategies with different perspectives. In the DVBA, there are only two bats: explorer and exploiter bat. While the explorer bat explores the search space, the exploiter bat makes an intensive search of the local with the highest probability of locating the desired target. Depending on their location, bats exchange the roles dynamically. The performance of the DVBA is extensively evaluated on a suite of 30 bound-constrained optimization problems from Congress of Evolutionary Computation (CEC) 2014 and compared with 4 classical optimization algorithm, 4 state-of-the-art modified bat algorithms, and 5 algorithms from a special session at CEC2014. In addition, DVBA is tested on supply chain cost problem to see its performance on a complicated real world problem. The experimental results demonstrated that the proposed DVBA outperform, or is comparable to, its competitors in terms of the quality of final solution and its convergence rates.Epoka Universit

Dynamic virtual bats algorithm (DVBA) for minimization of supply chain cost with embedded risk

Dynamic Virtual Bats Algorithm (DVBA) is a new optimization algorithm, which is tested on several benchmark functions for global optimization. However it has not been tested on a real world problem yet. In this paper DVBA has been applied to minimize the supply chain cost with other well known algorithms, Particle Swarm Optimization (PSO), Bat Algorithm (BA), Genetic Algorithm (GA) and Tabu Search (TS). Optimization of supply chain is considered as a real challenge by researchers because of its complexity. Big number of parameters to be controlled and their distributions, interconnections between parameters and dynamism are the main factors that increase the complexity of a supply chain. The result of the case study showed that the DVBA is much superior to other algorithms in terms of accuracy and efficiency. © 2014 IEEE