Performance Enhancement Of Artificial Bee Colony Optimization Algorithm

Artificial Bee Colony (ABC) algorithm is a recently proposed bio-inspired optimization algorithm, simulating foraging phenomenon of honeybees. Although literature works have revealed the superiority of ABC algorithm on numerous benchmark functions and real-world applications, the standard ABC and its variants have been found to suffer from slow convergence, prone to local-optima traps, poor exploitation and poor capability to replace exhaustive potential-solutions. To overcome the problems, this research work has proposed few modified and new ABC variants; Gbest Influenced-Random ABC (GRABC) algorithm systematically exploits two different mutation equations for appropriate exploration and exploitation of search-space, Multiple Gbest-guided ABC (MBABC) algorithm enhances the capability of locating global optimum by exploiting so-far-found multiple best regions of a search-space, Enhanced ABC (EABC) algorithm speeds up exploration for optimal-solutions based on the best so-far-found region of a search-space and Enhanced Probability-Selection ABC (EPS-ABC) algorithm, a modified version of the Probability-Selection ABC algorithm, simultaneously capitalizes on three different mutation equations for determining the global-optimum. All the proposed ABC variants have been incorporated with a proposed intelligent scout-bee scheme whilst MBABC and EABC employ a novel elite-update scheme

Krisis Demokrasi di Malaysia Kes Parti Islam SeMalaysia (PAS) dalam pilihan Raya 1969

Dalam sejarah Pilihan Raya, Malaysia memiliki pengalaman gelap iaitu terjadinya krisis demokrasi dalam pilihan raya 1969. Dalam pilihan raya 1969 ini bukan saja terjadi persaingan antara parti-parti politik yang ikut dalam pilihan raya tetapi juga terjadi rusuhan etnik yang dikenali dengan kerusuhan Mei 1969 yang membabitkan etnik Melayu dan China

An ABC Algorithm with Recombination

Artificial bee colony (ABC) is an efficient swarm intelligence algorithm, which has shown good exploration ability. However, its exploitation capacity needs to be improved. In this paper, a novel ABC variant with recombination (called RABC) is proposed to enhance the exploitation. RABC firstly employs a new search model inspired by the updating equation of particle swarm optimization (PSO). Then, both the new search model and the original ABC model are recombined to build a hybrid search model. The effectiveness of the proposed RABC is validated on ten famous benchmark optimization problems. Experimental results show RABC can significantly improve the quality of solutions and accelerate the convergence speed

A novel hybrid algorithm for mean-CVaR portfolio selection with real-world constraints

In this paper, we employ the Conditional Value at Risk (CVaR) to measure the portfolio risk, and propose a mean-CVaR portfolio selection model. In addition, some real-world constraints are considered. The constructed model is a non-linear discrete optimization problem and difficult to solve by the classic optimization techniques. A novel hybrid algorithm based particle swarm optimization (PSO) and artificial bee colony (ABC) is designed for this problem. The hybrid algorithm introduces the ABC operator into PSO. A numerical example is given to illustrate the modeling idea of the paper and the effectiveness of the proposed hybrid algorithm

ORDER BASED EMIGRANT CREATION STRATEGY FOR PARALLEL ARTIFICIAL BEE COLONY ALGORITHM

Artificial Bee Colony (ABC) algorithm inspired by the foraging behaviors of real honey bees is one of the most important swarm intelligence based optimization algorithms. When considering the robust and phase divided structure of the ABC algorithm, it is clearly seen that ABC algorithm is intrinsically suitable for parallelization. In this paper, we proposed a new emigrant creation strategy for parallel ABC algorithm. The proposed model named order based emigrant creation strategy depends on sending best food source in a subcolony after modifying it with another food source chosen sequentially from the same subcolony at each migration time. Experimental studies on a set of numerical benchmark functions showed that parallel ABC algorithm powered by the newly proposed model significantly improved quality of the final solutions and convergence performance when compared with standard serial ABC algorithm and parallel ABC algorithm for which the best food sources in the subcolonies directly used as emigrants