Clustering is the unsupervised learning in which the data is divided into similar groups (cluster) without any prior knowledge. The emerging swarm-based algorithms become an alternative to the conventional clustering methods to enhance the quality of results. Artificial Bee Colony (ABC) Algorithm is one of the Swarm Intelligent based optimization algorithm that exhibit foraging properties of a ...

searching efficiency and handling time are two major components of functional response and are usually used to evaluate effectiveness of natural enemies. the effect of different foraging periods on the functional response of larval aphidoletes aphidimyza (rondani) (dip.: cecidomyiidae) feeding on third instar nymphs of aphis craccivora was studied. the experiment was conducted in terms of time-...

We are interested in continuous foraging with multi-agent teams, where resources are replenished over time, and the goal is to maximize the rate of foraging. Existing algorithms for continuous foraging and area sweeping typically consider homogeneous agents. We are interested in heterogeneous teams, where agents have radically different capabilities. In particular, we consider two types of agen...

The Bacterial Foraging Optimization (BFO) is one of the metaheuristics algorithms that most widely used to solve optimization problems. The BFO is imitated from the behavior of the foraging bacteria group such as Ecoli. The main aim of algorithm is to eliminate those bacteria that have weak foraging methods and maintaining those bacteria that have strong foraging methods. In this extent, each b...

This study presents a new evolutionary learning algorithm to optimize the parameters of the neural fuzzy classifier (NFC). This new evolutionary learning algorithm is based on a hybrid of bacterial foraging optimization and particle swarm optimization. It is thus called bacterial foraging particle swarm optimization (BFPSO). The proposed BFPSO method performs local search through the chemotacti...

In order to solve the analysis problem more efficiently and quickly, we presented a hybrid method based on LS-SVM and Bacterial Foraging Optimization (BFO) in this study.The social foraging behavior of Escherichia coli bacteria has been used to solve optimization problems. This paper proposes a hybrid approach involving Bacteria Foraging Optimization Algorithm (BFO) and LS-SVM algorithms for pr...

A key challenge in designing robot teams is determining how to allocate team members to specific roles according to their abilities and the demands of the environment. In this paper we explore this issue in the context of multi-robot foraging, and we show that optimal foraging theory can be used to evaluate our work in learned multi-robot foraging tasks. We present a means by which members of a...