The objective of this chapter is to promote the bee colony optimization (BCO) algorithm as a
simple and effective metaheuristic method. BCO is a stochastic, random-search population …

Different methods have been used to train radial basis function (RBF) neural networks. This
paper proposes the use of a bee-inspired algorithm, named cOptBees, plus a heuristic to …

Vehicle routing problems constitute a class of combinatorial optimization tasks that search
for optimal routes (eg, minimal cost routes) for one or more vehicles to attend a set of nodes …

This paper reports how OptBees, an algorithm inspired by the collective decision-making of
bee colonies, performed in the test bed developed for the Special Session & Competition on …

The literature is now filled with swarm intelligence algorithms developed by taking
inspiration from a number of insects and other animals and phenomena, such as ants …

The bee colony optimization (BCO) algorithm with a linear dance function (denoted as the
BCO‐Linear algorithm) is inspired by the bees' foraging behaviors, in which waggle dances …

Intelligent agents called pricebots provide a convienient mechanism for implementing
automated dynamic pricing algorithms for sellers in an online economy. Pricebots enable an …

The Traveling Salesman Problem (TSP) is a combinatorial optimization problem widely used
to test new heuristics. Bee-inspired algorithms are receiving great attention from the Swarm …

The unique foraging behaviour of bees via waggle dance has been computationally realised
as an algorithm named bee colony optimisation (BCO) to solve different types of …

Multiobjective clustering techniques have been used to simultaneously consider several
complementary aspects of clustering quality. They optimize two or more cluster validity …