Investment decision making is one of the key issues in financial management. Selecting the appropriate tools and techniques that can make optimal portfolio is one of the main objectives of the investment world. This study tries to optimize the decision making in stock selection or the optimization of the portfolio by means of the artificial colony of honey bee algorithm. To determine the effect...

In this paper proposes a fuzzy multi-objective hybrid Genetic and Bee colony optimization algorithm(GA-BCO) to find the optimal restoration of loads of power distribution network under fault.Restoration of distribution systems is a complex combinatorial optimization problem that should beefficiently restored in reasonable time. To improve the efficiency of restoration and facilitate theactivity...

Artificial bee colony algorithms belong to the paradigm of bio-inspired, population-based, algorithms that have been widely used to solve optimization problems. These algorithms use population of individuals/particles/bees/ants in order to explore a search space of potential solutions to a given problem and to be able to quickly converge to a global solution, or at least to a good solution. The...

Low frequency oscillation problems are very difficult to solve because power systems are very large, complex and geographically distributed. Therefore, it is necessary to utilize most efficient optimization methods to take full advantages in simplifying the problem and its implementation. From this perspective, many successful and powerful optimization methods and algorithms have been employed ...

This paper surveys the intersection of two fascinating and increasingly popular domains: swarm intelligence and optimization. Whereas optimization has been popular academic topic for decades, swarm intelligence is relatively new subfield of artificial intelligence which studies the emergent collective intelligence of groups of simple agents. It is based on social behavior that can be observed i...

Artificial Bee Colony (ABC) is a distinguished optimization strategy that can resolve nonlinear and multifaceted problems. It is comparatively a straightforward and modern population based probabilistic approach for comprehensive optimization. In the vein of the other population based algorithms, ABC is moreover computationally classy due to its slow nature of search procedure. The solution exp...

In this paper, the previously proposed Predictive Energy Efficient Bee-inspired Routing (PEEBR) family of routing optimization algorithms based on the Artificial Bees Colony (ABC) Optimization model is extended from a random static mobility model, as employed by its first version (PEEBR1), into a random mobility model in its second version (PEEBR2). This random mobility model used by PEEBR-2 al...

Artificial Bee Colony (ABC) is a swarm optimization technique. This algorithm generally used to solve nonlinear and complex problems. ABC is one of the simplest and up to date population based probabilistic strategy for global optimization. Analogous to other population based algorithms, ABC also has some drawbacks computationally pricey due to its sluggish temperament of search procedure. The ...

We have already proposed a similarity-based mating scheme to recombine extreme and similar parents for evolutionary multiobjective optimization. In this paper, we examine the effect of the similarity-based mating scheme on the performance of evolutionary multiobjective optimization (EMO) algorithms. First we examine which is better between recombining similar or dissimilar parents. Next we exam...

In temperate regions of the world dominated by intensive agriculture, cities harbor a rich diversity and abundance bee species, often exceeding those rural environment. less industrialized tropical countries, in contrast, stressful conditions may exist for bees with perennial colonies such as stingless because lack resources amenity green spaces (flowers) appropriate nesting sites. Yet, we curr...