This paper empirically evaluates four meta-heuristic search techniques namely particle swarm optimization, artificial bee colony algorithm, Genetic Algorithm and Big Bang Big Crunch Algorithm for automatic test data generation for procedure oriented programs using structural symbolic testing method. Test data is generated for each feasible path of the programs. Experiments on ten benchmark prog...

Artificial bee colony (ABC) is a relatively new swarm intelligence based metaheuristic. It was successfully applied to various, mostly continuous, optimization problems. For all such heuristically guided search algorithms balance between exploitation and exploration is the determining factor for success. It is generally considered that in the ABC algorithm exploitation is performed by employed ...

Tuning of the system and the controller parameters always constituted an important part of the control applications in power system. These processes need complex mathematical calculations including derivative elements classically. However, these nowadays accomplished more easily and in shorter times, using by iterative optimization approaches such as evolutionary or swarm intelligence based met...

In this paper, a constrained mean-variance model is constructed for the portfolio optimization problems. The model is a mixed quadratic integer programming problem, and it is too hard to solve by using the traditional optimal algorithms. The purpose of this paper is to use a heuristic algorithm to solve this problem. Combined with the differential evolution strategy, a new hybrid artificial bee...

Swarm intelligence is a research branch that models the population of interacting agents or swarms that are able to self-organize. An ant colony, a flock of birds or an immune system is a typical example of a swarm system. Bees’ swarming around their hive is another example of swarm intelligence. The Artificial Bee Colony algorithm is an optimization algorithm based on the intelligent behavior ...

Nature inspired metaheuristics proved to be very successful when applied to hard optimization problems, combinatorial as well as global. For all these algorithms, with very different basic ideas, parameters and implementation details, the common problem that ultimately determines the successfulness of a particular algorithm is balance between exploitation and exploration. Exploitation refers to...

In the present study, in order to predict the activity coefficient of inorganic ions, 12 cases of aqueous chloride solution were considered (AClx=1,2; A=Li, Na, K, Rb, Mg, Ca, Ba, Mn, Fe, Co, Ni). For this study, the UNIQUAC thermodynamic model is desired and its adjustable parameters are optimized with the Genetic + PSO algorithm. The optimization of the UNIQUAC model with PSO+ genetic algorit...

Various natural systems teach us that very simple individual organisms can create systems able to perform highly complex tasks by dynamically interacting with each other. The Bee Colony Optimization Metaheuristic (BCO) is proposed in this paper. The artificial bee colony behaves partially alike, and partially differently from bee colonies in nature. The BCO is capable to solve deterministic com...

This research explains airplane model with two vertical vibrations for airframe and landing gear system. The purpose of this work is to advance vibrational model for study of adjustable vibration absorber and to plan Proportional-Integration-Derivative approach for adapting semi active control force. The coefficients of this method are modified as stated by Bee multiobjective optimization using...

Grid computing is a new computing model; it’s performing bulk operations among the connected network. Since it is possible to run multiple applications simultaneously may require multiple resources but often do not have the resources that time scheduling system is essential for allocating resources. Scheduling algorithms must be designed for Maximize utilization of the resources and minimize th...