Showing papers by "Milan Tuba published in 2011"

Modified cuckoo search algorithm for unconstrained optimization problems

Abstract: This paper presents modified cuckoo search (CS) algorithm for unconstrained optimization problems. Young and Deb's cuckoo search algorithm was successfully used on some optimization problems and there is also a corresponding code. We implemented a modified version of this algorithm where the step size is determined from the sorted rather than only permuted fitness matrix. Our modified algorithm was tested on eight standard benchmark functions. Comparison of the pure cuckoo search algorithm and our modified one is presented and it shows improved results by our modification.

An ant colony optimization algorithm with improved pheromone correction strategy for the minimum weight vertex cover problem

Abstract: The minimum weight vertex cover problem is an interesting and applicable NP-hard problem that has been investigated from many different aspects. The ant colony optimization metaheuristic is a relatively new technique that was successfully adjusted and applied to many hard combinatorial optimization problems, including the minimum weight vertex cover problem. Some kind of hybridization or exploitation of the knowledge about specific problem often greatly improves the performance of standard evolutionary algorithms. In this article we propose a pheromone correction heuristic strategy that uses information about the best-found solution to exclude suspicious elements from it. Elements are suspicious if they have some undesirable properties that make them unlikely members of the optimal solution. This hybridization improves pure ant colony optimization algorithm by avoiding early trapping in local convergence. We tested our algorithm on numerous test-cases that were used in the previous research of the same problem and our algorithm uniformly performed better, giving slightly better results in significantly shorter time.

Different approaches in parallelization of the artificial bee colony algorithm

Abstract: Parallel processing is gaining popularity due to the low cost of multi-core processors. In this paper we propose three different approaches in parallelization of standard artificial bee colony (ABC) algorithm. ABC algorithm was successfully used on many optimization problems, unconstrained and constrained. Our three approaches are independent parallel runs and two variations of multiple swarms parallelization. By using independent parallel runs method we succeeded in achieving faster execution of algorithm since multicore processors can be better utilized. By using multiple swarms technics with some modifications we also obtained better results than the original ABC algorithm. Different types of communications among swarms are proposed and examined. These methods of communication between swarms improved results and allowed adjustments of different ratios between exploration and exploitation. Set of eleven standard benchmark functions was used to test execution speed and quality of results improvements. Keywords—Artificial bee colony, Metaheuristic optimization, Parallelization, Swarm intelligence, Nature inspired metaheuristic algorithms.

Performance of the improved artificial bee colony algorithm on standard engineering constrained problems

Abstract: —Artificial bee colony (ABC) algorithm is successfully used for many hard, mostly continuous, optimization problems. There is a way to extend standard ABC algorithm to constrained problems. In this paper an improved version of the artificial bee colony algorithm adjusted for constrained optimization problems is presented. It uses Deb's rule. This modified algorithm has been implemented and tested on four standard engineering constrained benchmark problems which contain discrete and continuous variables. Our results were compared to the results obtained by simple constrained particle swarm optimization algorithm (SiC-PSO) which showed a very good performance when it was applied to the same problems. Our results are of the comparable quality with faster convergence.

Modified artificial bee colony algorithm for constrained problems optimization

Abstract:  Abstract—Original Karaboga's artificial bee colony (ABC) algorithm was applicable to unconstrained problems only and modifications for constrained problems were introduced later. In this article we propose an improved artificial bee colony algorithm for constrained problems. Since the ABC algorithm for constrained problems does not consider the initial population to be feasible we introduced a modification, besides penalty function and Deb's rule, in a form of ―smart bee‖ (SB) which uses its historical memories for the location and quality of food sources. This modified SB-ABC algorithm was tested on standard benchmark functions for constrained optimization problems and proved to be better. Keywords—Artificial bee colony, Constrained optimization, Nature inspired metaheuristic algorithms, Swarm intelligence.

Guided artificial bee colony algorithm

Abstract: Artificial bee colony algorithm is an optimization algorithm based on a particular intelligent behaviour of honeybee swarms. In this paper we present a novel algorithm named GABC which integrates artificial bee colony algorithm (ABC) with self-adaptive guidance adjusted for engineering optimization problems. The novel algorithm speeds up the convergence and improves the algorithm's exploitation. We tested our guided algorithm on four standard engineering benchmark problems. The experimental results show that GABC algorithm can outperform ABC algorithm in most of the cases.

Performance of object-oriented software system for improved artificial bee colony optimization

Abstract:  Abstract— Artificial bee colony (ABC) metaheuristic algorithm introduced by Karaboga was successfully used on many continuous optimization problems. There is also a corresponding program written in C. This article describes an object-oriented software system for improved artificial bee colony algorithm written in C# with corresponding flexible graphical user interface (GUI). Since this implementation is object-oriented it is easier for maintenance and it uses threads which significantly increases execution speed on multicore processors. The application was successfully tested on standard benchmark problems.