a swarm intelligence-based optimizer for molecular geometry

We present a stochastic, swarm intelligence-based optimization algorithm for the prediction of global minima on potential energy surfaces of molecular clusterstructures. Our optimization approach is a modification of the artificial bee colony (ABC) algorithm which is inspired by the foraging behavior of honey bees. We apply our modified ABC algorithm to the problem of global geometryoptimization of molecular clusterstructures and show its performance for clusters with 2–57 particles and different interatomic interaction potentials

Amélioration des Performances de Certaines Méthodes de Calcul Numérique a L'aide des Algorithmes Evolutionnaires

In this work, we prove that the Gregory Formula (G) can be optimized by minimizing some of their coefficients in the remainder term by using Artificial Bee Colony (ABC) Algorithm. Experimental tests prove that obtained Formula can be rendered a powerful formula for library us

Robust fuzzy PSS design using ABC

This paper presents an Artificial Bee Colony (ABC) algorithm to tune optimal rule-base of a Fuzzy Power System Stabilizer (FPSS) which leads to damp low frequency oscillation following disturbances in power systems. Thus, extraction of an appropriate set of rules or selection of an optimal set of rules from the set of possible rules is an important and essential step toward the design of any successful fuzzy logic controller. Consequently, in this paper, an ABC based rule generation method is proposed for automated fuzzy PSS design to improve power system stability and reduce the design effort. The effectiveness of the proposed method is demonstrated on a 3-machine 9-bus standard power system in comparison with the Genetic Algorithm based tuned FPSS under different loading condition through ITAE performance indices

An Effective Hybrid Butterfly Optimization Algorithm with Artificial Bee Colony for Numerical Optimization

In this paper, a new hybrid optimization algorithm which combines the standard Butterfly Optimization Algorithm (BOA) with Artificial Bee Colony (ABC) algorithm is proposed. The proposed algorithm used the advantages of both the algorithms in order to balance the trade-off between exploration and exploitation. Experiments have been conducted on the proposed algorithm using ten benchmark problems having a broad range of dimensions and diverse complexities. The simulation results demonstrate that the convergence speed and accuracy of the proposed algorithm in finding optimal solutions is significantly better than BOA and ABC

ENHANCED SEEKER OPTIMIZATION ALGORITHM FOR REDUCTION OF ACTIVE POWER LOSS

This paper projects Enhanced Seeker Optimization (ESO) algorithm for solving optimal reactive power problem. Seeker optimization algorithm (SOA) models the deeds of human search population based on their memory, experience, uncertainty reasoning and communication with each other. In Artificial Bee Colony (ABC) algorithm the colony consists of three groups of bees: employed bees, onlookers and scouts. All bees that are presently exploiting a food source are known as employed bees. The number of the employed bees is equal to the number of food sources and an employed bee is allocated to one of the sources. In this paper hybridization of the seeker optimization algorithm with artificial bee colony (ABC) algorithm has been done to solve the optimal reactive power problem. Enhanced Seeker Optimization (ESO) algorithm combines two different solution exploration equations of the ABC algorithm and solution exploration equation of the SOA in order to progress the performance of SOA and ABC algorithms. At certain period’s seeker’s location are modified by search principles obtained from the ABC algorithm, also it adjust the inter-subpopulation learning phase by using the binomial crossover operator. In order to evaluate the efficiency of proposed Enhanced Seeker Optimization (ESO) algorithm it has been tested in standard IEEE 57,118 bus systems and compared to other specified algorithms. Simulation results clearly indicate the best performance of the proposed Enhanced Seeker Optimization (ESO) algorithm in reducing the real power loss and voltage profiles are within the limits

Optimizing the Gains of PD Controller Using Artificial Bee Colony for Controlling the Rigid Gantry Crane System

Control position and reduction of swinging of the payload of a rigid gantry crane system is a challenging work because of under-actuated system. This paper addresses challenges by proposing the artificial bee colony (ABC) algorithm to optimize the gains of the PD controller to form what the so-called the artificial bee colony (ABC)-PD controller. The effectiveness of the proposed control algorithm is tested under constant step functions and compared with Ziegler-Nichols (ZN)-PD controller. The results show that the proposed controller produces slower rise time and peak time, but faster settling time than the ZN-PD controller as well as no overshoot under the predefined trajectories

Flowshop scheduling using artificial bee colony (ABC) algorithm with varying onlooker bees approaches

The common objectives in permutation flowshop scheduling problem are to minimize the total completion time or formally called as makespan and tardiness. Artificial Bee Colony (ABC) algorithm is one of the methods used to solve the flowshop scheduling problem but only a few researches have been found using this method in this area. Therefore, ABC algorithm is proposed to solve the flowshop scheduling problem in this research. The main objective of this research is to develop a computer program with capability of manipulating the onlooker bee approaches in ABC Algorithm for solving flowshop scheduling problem. The research also analyzes the performance of the ABC algorithm using three different onlooker bee approaches. A simulation computer program was developed using Visual Basic Editor in Microsoft excel 2007. In this simulation, onlooker bees as the important bee make decision to choose the specific method. The performance of the ABC algorithm was evaluated through three different onlooker approaches i.e. method 3+0+0 (three onlooker bees are dedicated to the best employee bee), method 2+1+0 (two onlooker bees are dedicated to the best employee bee and one onlooker bee is dedicated to second best employee bee) and method 1+1+1 (one onlooker bee is dedicated to each employee bee). All the average percentage makespan difference from three onlooker approaches was compared and the lowest average percentage makespan difference was selected as the best method. The simulation results indicated that method 2+1+0 produces best result at low iterations of 102 and below. At high iterations of 204 and above, method 3+0+0 dominates the best performance. Based on this finding, the selection of the best method can be decided based on the iteration time available. If iteration available is long, method 3+0+0 is more appropriate, otherwise method 2+1+0 is the best choice. The findings from this research can be used by system developer or computer programmer to search the optimum sequence during the manufacturing process and improve the flowshop scheduling

A hybrid swarm-based algorithm for single-objective optimization problems involving high-cost analyses

In many technical fields, single-objective optimization procedures in continuous domains involve expensive numerical simulations. In this context, an improvement of the Artificial Bee Colony (ABC) algorithm, called the Artificial super-Bee enhanced Colony (AsBeC), is presented. AsBeC is designed to provide fast convergence speed, high solution accuracy and robust performance over a wide range of problems. It implements enhancements of the ABC structure and hybridizations with interpolation strategies. The latter are inspired by the quadratic trust region approach for local investigation and by an efficient global optimizer for separable problems. Each modification and their combined effects are studied with appropriate metrics on a numerical benchmark, which is also used for comparing AsBeC with some effective ABC variants and other derivative-free algorithms. In addition, the presented algorithm is validated on two recent benchmarks adopted for competitions in international conferences. Results show remarkable competitiveness and robustness for AsBeC.Comment: 19 pages, 4 figures, Springer Swarm Intelligenc