In this thesis we present a new algorithm for the Vehicle Routing Problem called the Enhanced Bees Algorithm. It is adapted from a fairly recent algorithm, the Bees Algorithm, which was developed for continuous optimisation problems. We show that the results obtained by the Enhanced Bees Algorithm are competitive with the best meta-heuristics available for the Vehicle Routing Problem—it is able...

Abstract— In this paper we have tried to develop an altered version of the artificial bee colony algorithm which is inspired from and combined with the meta-heuristic algorithm of firefly. In this method, we have tried to change the main equation of searching within the original ABC algorithm. On this basis, a new combined equation was used for steps of employed bees and onlooker bees. For this...

In this paper, we present QoS bee routing protocol (QBR), a new routing algorithm based on the characteristics of Ad hoc networks and the bee colony algorithm. The new routing protocol is inspired by the foraging behavior of bees in nature, which can find the solution of global optimal value very quickly with the characteristic that every bee is able to search local optimal solution. Biological...

In this paper, a recurrent neural network based fuzzy inference system (RNFIS) for prediction is proposed. A recurrent network is embedded in the RNFIS by adding feedback connections in the second layer and in the output layer. In this paper, a hybrid learning algorithm is used to train the RNFIS model. This algorithm uses a modified version of the bees algorithm and gradient descent (GD). In t...

This paper proposes Hybrid SFL-Bees Algorithm that combines strengths of Shuffled Frog Leaping Algorithm (SFLA) and Bees Algorithms (BA). While SFLA can find optimal solutions quickly because of directive searching and exchange of information, BA has higher random that make it easily escape local optima to find global solutions. Thus Hybrid SFL-Bees Algorithm is able to find optimal solutions q...

This Chapter summarizes some of the well known stigmergic computational techniques inspired by nature, mainly for optimization problems developed by mimicking social insects’ behavior. Some facts about social insects namely ants, bees and termites are presented with an emphasis on how they could interact and self organize for solving real world problems. We focused on ant colony optimization al...

A new population-based search algorithm called the Bees Algorithm (BA) is presented in this paper. The algorithm mimics the food foraging behavior of swarms of honey bees. This algorithm performs a kind of neighborhood search combined with random search and can be used for both combinatorial optimization and functional optimization and with good numerical optimization results. ABC is a meta-heu...