Swam intelligence is an emerging field in Artificial Intelligence. The living nature and life style of animals, birds and other living organisms can be inherited and applied to solve many real world problems. ABC is a recently developed swam intelligence algorithm developed by Dervis Karaboga in the year 2005.In ABC, foraging is one of the behavior of honey bees to search, collect food from its...

The Artificial Bee Colony (ABC) algorithm is a popular swarm based algorithm inspired by the intelligent foraging behavior of honey bees. In the past, many swarm intelligence based techniques were introduced and proved their effective performance in solving various optimization problems. The exploitation of food sources is performed by onlooker bees in accordance with a proportional selection s...

Autonomous systems assume intelligent behaviour with capabilities of dealing in complex and changing environments. Problem of path planning, which can be observed as an optimization problem, seems to be of high importance for arising of intelligent behaviour for different real-world problem domains. Swarm intelligence has gained increasingly high interest among the researchers from different ar...

Microbial strains can be manipulated to improve product yield and improve growth characteristics. Optimization algorithms are developed to identify the effects of gene knockout on the results. However, this process is often faced the problem of being trapped in local minima and slow convergence due to repetitive iterations of algorithm. In this paper, we proposed Bees Hill Flux Balance Analysis...

A novel Cooperative Bee Swarm Optimization (CBSO) algorithm based on foraging behaviour of honey bees is presented. The CBSO employs cooperative behaviours of multiple swarms in optimizing numerical functions. The proposed approach provides different patterns which are used by the bees to adjust their flying trajectories. The flying patterns provide an efficient way to balance exploration and e...

The Traveling Salesman Problem (TSP) is an NP-hard problem that has been used as a significant testing ground for many algorithms. It is also a problem that appears in many real industrial problems. This paper describes a Bee Colony Optimization (BCO) algorithm with a fragmentation state transition rule (FSTR) for TSP. The FSTR in the BCO algorithm helps bees to produce a tour by combining a fe...

rotating machinery is the most common machinery in industry. the root of the faults in rotating machinery is often faulty rolling element bearings. this paper presents a technique using optimized artificial neural network by the bees algorithm for automated diagnosis of localized faults in rolling element bearings. the inputs of this technique are a number of features (maximum likelihood estima...

Case-Based Reasoning (CBR) has achieved a considerable interest from researchers for solving non-trivial or ill-defined problems such as those encountered by project managers including support for software project management in predictions and lesson learned. Software effort estimation is the key factor for successful software project management. In particular, the use of CBR for effort estimat...

Cloud computing is known as digital service delivery over the Internet by several applications which are carried out by computer systems in distributed datacenters. It supplies a high performance computing based on protocols which allow shared computation and storage over long distances. In this paper, we present a new Bee Swarm optimization algorithm called Bees Life Algorithm (BLA) applied to...

The marriage in honey–bees optimization (MBO) algorithm was recently proposed and showed good results for combinatorial optimization problems. Contrary to most of the swarm intelligence algorithms (such as Ant Colony Optimization), MBO uses self-organization to mix different heuristics. This paper presents a variation of the MBO algorithm where the colony contains a single queen with multiple w...