A Honey Bee Mating Optimization (HBMO) technique is proposed for the optimal tuning of the Power System Stabilizer (PSS) in this paper. The design problem of robustly selecting PSS parameters is formulated as an optimization problem according to the time domain-based objective function which is solved by the HBMO technique that has a strong ability to find the most optimistic results. To achiev...

Honey Bee Mating Optimization (HBMO) is a recent swarm-based optimization algorithm to solve highly nonlinear problems, whose based approach combines the powers of simulated annealing, genetic algorithms, and an effective local search heuristic to search for the best possible solution to the problem under investigation within a reasonable computing time. In this work, the HBMO-based design is c...

As a nonlinear, non-convex and large scale optimization problem including both continuous and discrete control variables, security-constrained optimal power flow (SC-OPF) is one of the most important power system problems which need a superior optimization technique to be effectively solved. Inspired by the mating process of honey bees, honey bee mating optimization (HBMO) is a recently invente...

Image entropy thresholding approach has drawn the attentions in image segmatation. The endeavor of this paper is focused on multilevel thresholding using the minimum cross enrtop criterion. In the literature, the particle swarm optimization (PSO) had been applied to conducting the thresold selection. The adopted algorithm used in this paper is the honey bee mating optimization (HBMO). In experi...

This paper introduces a new hybrid algorithmic nature inspired approach based on the concepts of the Honey Bees Mating Optimization Algorithm (HBMO) and of the Greedy Randomized Adaptive Search Procedure (GRASP), for optimally clustering N objects into K clusters. The proposed algorithm for the Clustering Analysis, the Hybrid HBMO-GRASP, is a two phase algorithm which combines a HBMO algorithm ...

Over the last decade, evolutionary and meta-heuristic algorithms have been extensively used as search and optimization tools in various problem domains, including science, commerce, and engineering. Their broad applicability, ease of use, and global perspective may be considered as the primary reason for their success. Honey bees mating process may also be considered as a typical swarm-based ap...

The broad of applicability, ease of use, and global perspective of so-called meta-heuristic algorithms may be considered as the primary reason for their extensive application and success as search and optimization tools in various problem domains. Honey bees are among the most well-studied social insects. Their mating process may also be considered as a typical swarm-based approach to optimizat...

The minimum cross entropy thresholding (MCET) has been widely applied in image processing. In this paper, a new multilevel MCET algorithm based on the artificial bee colony (ABC) algorithm is proposed. The proposed thresholding algorithm is called ABC-based MCET algorithm. Four different methods including the exhaustive search, the honey bee mating optimization (HBMO), the particle swarm optimi...

So far, many attempts have been made to identify the relation between discharge and suspended sediment load.Empirical relations such as sediment rating curves are often applied to determine the average relationship between discharge and suspended sediment load. This type of models generally underestimates or overestimateswhen sediment load are estimated from water discharge using least squares ...