Numerous particle swarm optimization (PSO) algorithms have been developed for solving numerical problems in recent years. However, most of existing PSO only one search phase. There is no strengthened phase the well-performed particles, and also re-initialization exhausted particles. These issues may still restrict performance complex problems. In this paper, inspired by bee-foraging mechanism a...

A modified Artificial Bee Colony (ABC) metaheuristics optimization technique is applied for cancer classification, that reduces the classifier’s prediction errors and allows faster convergence by selecting informative genes. Cuckoo search (CS) algorithm was used in onlooker bee phase (exploitation phase) of ABC to boost performance maintaining balance between exploration exploitation ABC. Tuned...

Software testing plays a vital role during the software development process, as it ensures quality deployment. Success of depends on design effective test cases. To achieve optimization generated cases, proposed approach combines both global and local searches by means intelligent agents which exhibit behaviour employed bees, onlooker scout bees in qABC algorithm. The algorithm has key improvem...

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...

Worker assignment is a new type of problem in assembly line balancing problems, which typically occurs in sheltered work centers for the disabled. However, only a few contributions consider worker assignment in a two-sided assembly line. This research presents three variants of artificial bee colony algorithm to solve worker assignment and line balancing in two-sided assembly lines. The utiliza...

The standard artificial bee colony (ABC) algorithm involves exploration and exploitation processes which need to be balanced for enhanced performance. This paper proposes a new modified ABC algorithm named JA-ABC5 to enhance convergence speed and improve the ability to reach the global optimum by balancing exploration and exploitation processes. New stages have been proposed at the earlier stag...

This paper presents an Improved Artificial Bee Colony (IABC) algorithm to optimal tune of the Power System Stabilizer (PSS). In the IABC algorithm an interactive strategy is introduced by considering the universal gravitation to the movement of onlooker bees in the original Artificial Bee Colony (ABC) to enhance exploration ability of the ABC algorithm. The robustly PSS tuning problem is formul...

The multi-objective long-term economic dispatch in hydropower system is a complicated nonlinear optimization problem with a group of complex constraints which makes the optimization of conflict objectives through traditional methods a hard task. This paper is aim to present a novel multi-objective evolutionary algorithm named multiobjective artificial bee colony (MOABC) algorithm and compares t...

Image template matching refers to the technique of locating a given reference image over a source image such that they are the most similar. It is a fundamental mission in the field of visual target recognition. In general, there are two critical aspects of a template matching scheme. One is similarity measurement and the other is best-match location search. In this work, we choose the well-kno...

Today many fields like engineering, management, and economy faces many optimization problems which are needed to be solved by meta-heuristics techniques. The Artificial Bee Colony (ABC) algorithm is a population-based stochastic swarm intelligence algorithm finds applications in most of the fields. The problem with ABC is its slow convergence speed due to the poor exploitation capability and fa...