In this paper a hybrid model called Particle Swarm Artificial Bee Colony algorithm (PSABC) has been proposed. The PSABC algorithm is a combination of Particle Swarm Optimization (PSO) and Artificial Bee Colony (ABC) Algorithm. PSABC has been used in this context to optimize the fitness value of a population in ABC algorithm using Particle Swarm Optimization. PSABC could be used to optimize the ...

Artificial Bee Colony (ABC) algorithm simulates the foraging behavior of honey bee colonies. ABC is an optimization technique, which is used in finding the best solution from all feasible solutions. However, there is still an insufficiency in ABC regarding improvement in exploitation and convergence speed. In order to improve the performance of ABC we embedded PSO into ABC. As PSO has memory, k...

In this paper, an improved artificial bee colony (I-ABC) algorithm for the maximum power point tracking (MPPT) of a photovoltaic module array (PVMA) is presented. Even though P-V output characteristic curve with multi-peak was generated due to any damages or shading discovered on PVMA, I-ABC could get rid stuck local (LMPP), but quickly and stably track global (GMPP), thereby improving generati...

Artificial Bee colony is one of the most efficient optimizing technique introduced so far. Artificial Bee Colony simulates the brilliant behavior honey bee swarm. Considering the environmental impacts that grow from the emission produced by the conventional fuel power plants, the economic dispatch that minimizes only the total fuel cost can no longer be considered as single objective function. ...

In a global economy, an efficient supply chain as a main core competency empowers enterprises to provide products or services at the right time in the right quantity, at a low cost. This paper is to plan a single product, multi-echelon, multi-period closed loop supply chain for high-tech products (which have continuous price decrease). Ultimately, considering components rated to procurement, pr...

This paper proposes a γ-Artificial Bee Colony – Feature Selection (γ-ABC-FS) approach to identify the salient feature subset that improves classification accuracy. γ-ABC-FS hybridizes Artificial Colony(ABC) algorithm by employing Rough Set Theory concepts in ABC search process and improved initialization local strategies. The proposed is intended begin evolutionary not excluding attributes cont...

Quantitative Structure–Activity Relationship (QSAR) aims to correlate molecular structure properties with corresponding bioactivity. Chance correlations and multicollinearity are two major problems often encountered when generating QSAR models. Feature selection can significantly improve the accuracy interpretability of by removing redundant or irrelevant descriptors. An artificial bee colony a...

Portfolio selection (optimization) problem is a very important and widely researched problem in the areas of finance and economy. Literature review shows that many methods and heuristics were applied to this hard optimization problem, however, there are only few implementations of swarm intelligence metaheuristics. This paper presents artificial bee colony (ABC) algorithm applied to the cardina...

Maximum Likelihood (ML) method has an excellent performance for Direction-Of-Arrival (DOA) estimation, but a multidimensional nonlinear solution search is required which complicates the computation and prevents the method from practical use. To reduce the high computational burden of ML method and make it more suitable to engineering applications, we apply the Artificial Bee Colony (ABC) algori...

In this paper we propose a mechanism for enhancing the performance of the Artificial Bee Colony Algorithm (ABCA) by making use low discrepancy Sobol sequence. The performance of the proposed Sobol sequence guided ABC (S-ABC) is analyzed over several benchmark functions and also compared to that of basic ABC. The empirical results show that the presence of low discrepancy sequence like that of S...