scholarly journals Application of Heuristic and Metaheuristic Algorithms in Solving Constrained Weber Problem with Feasible Region Bounded by Arcs

2017 ◽  
Vol 2017 ◽  
pp. 1-13 ◽  
Author(s):  
Igor Stojanović ◽  
Ivona Brajević ◽  
Predrag S. Stanimirović ◽  
Lev A. Kazakovtsev ◽  
Zoran Zdravev
Keyword(s):  
Heuristic Algorithm ◽  
Optimization Problems ◽  
Continuous Optimization ◽  
Facility Location Problem ◽  
Metaheuristic Algorithms ◽  
Feasible Region ◽  
Weber Problem ◽  
Bee Colony ◽  
Feasible Solutions ◽  
Weiszfeld Procedure

The continuous planar facility location problem with the connected region of feasible solutions bounded by arcs is a particular case of the constrained Weber problem. This problem is a continuous optimization problem which has a nonconvex feasible set of constraints. This paper suggests appropriate modifications of four metaheuristic algorithms which are defined with the aim of solving this type of nonconvex optimization problems. Also, a comparison of these algorithms to each other as well as to the heuristic algorithm is presented. The artificial bee colony algorithm, firefly algorithm, and their recently proposed improved versions for constrained optimization are appropriately modified and applied to the case study. The heuristic algorithm based on modified Weiszfeld procedure is also implemented for the purpose of comparison with the metaheuristic approaches. Obtained numerical results show that metaheuristic algorithms can be successfully applied to solve the instances of this problem of up to 500 constraints. Among these four algorithms, the improved version of artificial bee algorithm is the most efficient with respect to the quality of the solution, robustness, and the computational efficiency.

scholarly journals A Modified Artificial Bee Colony Algorithm with Firefly Algorithm Strategy for Continuous Optimization Problems

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Amnat Panniem ◽  
Pikul Puphasuk
Keyword(s):  
Firefly Algorithm ◽  
Artificial Bee Colony ◽  
Optimization Problems ◽  
Continuous Optimization ◽  
Control Parameters ◽  
Exploration And Exploitation ◽  
Bee Colony ◽  
Continuous Problems ◽  
Local Solutions ◽  
Continuous Optimization Problems

Artificial Bee Colony (ABC) algorithm is one of the efficient nature-inspired optimization algorithms for solving continuous problems. It has no sensitive control parameters and has been shown to be competitive with other well-known algorithms. However, the slow convergence, premature convergence, and being trapped within the local solutions may occur during the search. In this paper, we propose a new Modified Artificial Bee Colony (MABC) algorithm to overcome these problems. All phases of ABC are determined for improving the exploration and exploitation processes. We use a new search equation in employed bee phase, increase the probabilities for onlooker bees to find better positions, and replace some worst positions by the new ones in onlooker bee phase. Moreover, we use the Firefly algorithm strategy to generate a new position replacing an unupdated position in scout bee phase. Its performance is tested on selected benchmark functions. Experimental results show that MABC is more effective than ABC and some other modifications of ABC.

Using Chemotherapy Science Algorithm (CSA) to Solve the Knapsack Problem

2018 ◽  
Vol 7 (1) ◽  
pp. 86-103
Author(s):  
Mohammad Hassan Salmani ◽  
Kourosh Eshghi
Keyword(s):  
Cell Size ◽  
Knapsack Problem ◽  
Heuristic Algorithm ◽  
Heuristic Algorithms ◽  
Optimization Problems ◽  
Cell Area ◽  
Collateral Damage ◽  
Cell Position ◽  
Feasible Solutions

Optimization, which, by definition, can help one find the best solution from all feasible solutions, has sometimes been an interesting and important area for research in science. Solving real and hard optimization problems calls for developing approximate, heuristic, and meta-heuristic algorithms. In this article, a new meta-heuristic algorithm is proposed on the basis of the chemotherapy method to cure cancers – this algorithm mainly searches the infeasible region. As in chemotherapy, this algorithm tries to kill unsatisfactory (especially infeasible) solutions (cancers cells); however, collateral damage is sometimes inevitable – some healthy, innocuous, and good cells might be targeted as well. Also, different conceptual terms including Cell Size, Cell Position, Cell Area, and Random Cells are presented and defined in this article. Furthermore, Chemotherapy Science Algorithm (CSA) and its structure are tested based on benchmark Knapsack Problem. Reported results show the efficiency of the proposed algorithm.

A HYBRID ARTIFICIAL BEE COLONY OPTIMIZATION AND QUANTUM EVOLUTIONARY ALGORITHM FOR CONTINUOUS OPTIMIZATION PROBLEMS

2010 ◽  
Vol 20 (01) ◽  
pp. 39-50 ◽  
Author(s):  
HAI-BIN DUAN ◽  
CHUN-FANG XU ◽  
ZHI-HUI XING
Keyword(s):  
Evolutionary Algorithm ◽  
Artificial Bee Colony ◽  
Optimization Problems ◽  
Continuous Optimization ◽  
Experimental Comparison ◽  
Artificial Bee Colony Optimization ◽  
Bee Colony ◽  
Comparison Results ◽  
Bee Colony Optimization ◽  
Continuous Optimization Problems

In this paper, a novel hybrid Artificial Bee Colony (ABC) and Quantum Evolutionary Algorithm (QEA) is proposed for solving continuous optimization problems. ABC is adopted to increase the local search capacity as well as the randomness of the populations. In this way, the improved QEA can jump out of the premature convergence and find the optimal value. To show the performance of our proposed hybrid QEA with ABC, a number of experiments are carried out on a set of well-known Benchmark continuous optimization problems and the related results are compared with two other QEAs: the QEA with classical crossover operation, and the QEA with 2-crossover strategy. The experimental comparison results demonstrate that the proposed hybrid ABC and QEA approach is feasible and effective in solving complex continuous optimization problems.

scholarly journals CONTOPT-JS: Metaheuristic Algorithms based JavaScript Software Library for Continuous Optimization Problems

2019 ◽  
pp. 1-7
Author(s):  
Osman Gokalp ◽  
Aybars Ugur ◽  
Sema Bodur
Keyword(s):  
Web Applications ◽  
Modular Design ◽  
Optimization Problems ◽  
Continuous Optimization ◽  
Software Library ◽  
Bee Colony ◽  
Experimental Works ◽  
Continuous Optimization Problems ◽  
Client Side ◽  
New Algorithms

In this study, a software library called CONTOPT-JS has been developed for solving continuous optimization problems. By using this JavaScript language based library, fully client-side web applications can be developed. In the library, Artificial Bee Colony, Differential Evolution, Particle Swarm Optimization and Evolution Strategies metaheuristics exist and new algorithms and new problems can be added because of its modular design. Using the CONTOPT-JS library, experimental works have been conducted on some standard optimization benchmark functions and Sensor Deployment application area and the obtained results have been presented.

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