Hybridizing ant colony optimization with firefly algorithm for unconstrained optimization problems

Rizk‐Allah, Rizk M.; Zaki, Elsayed M.; El‐Sawy, A. A.

doi:10.1016/j.amc.2013.07.092

Cited by 61 publications

(25 citation statements)

References 18 publications

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“…The main advantages of FA are namely intensification and diversification or exploitation and exploration: As light intensity decreases with distance, the attraction among fireflies can be local or global, depending on the absorbing coefficient, and thus all local modes as well as global modes will be visited. Therefore, it has captured much attention and has been successfully applied to solve several optimization problems including [7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Hybridization of Fruit Fly Optimization Algorithm and Firefly Algorithm for Solving Nonlinear Programming Problems

Allah¹

2016

Int J Swarm Intel Evol Comput

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We propose a novel hybrsid algorithm named, FOA-FA to solve the nonlinear programming problems (NLPPs). The main feature of the hybrid algorithm is to integrate the strength of fruit fly optimization algorithm (FOA) in handling continuous optimization and the merit of firefly algorithm (FA) in achieving robust exploration. The methodology of the proposed algorithm consists of two phases. The first one employs a variation on original FOA employing a new adaptive radius mechanism (ARM) for exploring the whole scope around the fruit flies locations to overcome the drawbacks of original FOA which has been continues for the nonnegative orthant problems. The second one incorporates FA to update the previous best locations of fruit flies to avoid the premature convergence. The hybrid algorithm speeds up the convergence and improves the algorithm's performance. The proposed FOA-FA algorithm is tested on several benchmark problems and two engineering applications. The numerical comparisons have demonstrated its effectiveness and efficiency.

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Section: Introductionmentioning

confidence: 99%

Hybridization of Fruit Fly Optimization Algorithm and Firefly Algorithm for Solving Nonlinear Programming Problems

Allah¹

2016

Int J Swarm Intel Evol Comput

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“…A promising new natureinspired algorithm known as FA was recently proposed and has gained more attention in the research literature. The ACOFA [13] is the new hybridization for FA and ACO [14] algorithm. This hybrid algorithm has been designed to solve unconstrained optimization problems and FA works as a local search to refine the positions found by the ants.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Algorithm Based on Firefly Algorithm and Differential Evolution for Global Optimization

Sarbazfard¹,

Jafarian²

2016

ijacsa

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Abstract-In this paper, a new and an effective combination of two metaheuristic algorithms, namely Firefly Algorithm and the Differential evolution, has been proposed. This hybridization called as HFADE, consists of two phases of Differential Evolution (DE) and Firefly Algorithm (FA). Firefly algorithm is the natureinspired algorithm which has its roots in the light intensity attraction process of firefly in the nature. Differential evolution is an Evolutionary Algorithm that uses the evolutionary operators like selection, recombination and mutation. FA and DE together are effective and powerful algorithms but FA algorithm depends on random directions for search which led into retardation in finding the best solution and DE needs more iteration to find proper solution. As a result, this proposed method has been designed to cover each algorithm deficiencies so as to make them more suitable for optimization in real world domain. To obtain the required results, the experiment on a set of benchmark functions was performed and findings showed that HFADE is a more preferable and effective method in solving the highdimensional functions.

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Section: Introductionmentioning

confidence: 99%

“…However, there are still a lot of problems to solve in the application of ant colony algorithm in multi-objective optimization. Fields worthy of exploration include how to select the initial ant colony, how to construct Pareto optimal solution set, how to set the parameters of any colony algorithm, how to conduct simulation experiment and the verification of related theories, etc [5].This paper first explained the basic principles of multi-objective problems and ant colony algorithm in detail, based on which, it provided the complete procedure of multi-objective ant colony optimization, and with the simulation, test and analysis of the standard test function, and …”

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confidence: 99%

Application of Ant Colony Algorithm in Multi-objective Optimization Problems

Tian

2015

TELKOMNIKA

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In actual application and scientific research, multi-objective optimization is an extremely important research subject. In reality, many issues are related to the simultaneous optimization under multi-objective conditions. The research subject of multi-objective IntroductionOriginated from the design, planing scale, project adjustment and other essential decision issues of many complex systems in real life, multi-objective optimization has always been one of the important subjects of engineering practice and scientific research. In the aspects of computing science, decision science and operation science, there once appeared much certainty, randomization methods specialized for multi-objective optimization [1]. In recent year, along with the improvement in the calculating speed and capability of calculation devices, the application of intelligent evolutionary algorithms in multi-objective optimization, as with genetic algorithm, genetic planing and genetic program designing, etc., has gained wide confirmation, which is mainly because these evolutionary algorithms process intelligence features of self-adaptivity, self-directed learning and self-organizing [2].In terms of multi-objective optimization, usually there are conflicts and restrains among different targets of the optimization problem. Therefore, in order to maintain balance among these targets, the research aim of the algorithm is to try best to locate the optimal set near the actual Pareto front. Considering the followed decision step, the solutions in the solution set should be distributed as evenly as possible to increase the diversity of the possible solution. Considering the effect of actual application, the time period of searching the Pareto solution set should be as short as possible [3]. Most of the current studies adopt genetic algorithm to explore multi-objective optimization, and the amount of studies adopting emerging intelligence algorithm, such as ant colony algorithm, to explore multi-objective optimization is quite limited. With characteristics of implicit parallelism and intelligence, ant colony algorithm is quite suitable for optimization. It can be seen from the current research results that ant colony algorithm has good performance. It is proved by practice that the application of ant colony algorithm in solving single-objective problems is very successful [4]. However, there are still a lot of problems to solve in the application of ant colony algorithm in multi-objective optimization. Fields worthy of exploration include how to select the initial ant colony, how to construct Pareto optimal solution set, how to set the parameters of any colony algorithm, how to conduct simulation experiment and the verification of related theories, etc [5].This paper first explained the basic principles of multi-objective problems and ant colony algorithm in detail, based on which, it provided the complete procedure of multi-objective ant colony optimization, and with the simulation, test and analysis of the standard test function, and

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Hybridizing ant colony optimization with firefly algorithm for unconstrained optimization problems

Cited by 61 publications

References 18 publications

Hybridization of Fruit Fly Optimization Algorithm and Firefly Algorithm for Solving Nonlinear Programming Problems

Hybridization of Fruit Fly Optimization Algorithm and Firefly Algorithm for Solving Nonlinear Programming Problems

A Hybrid Algorithm Based on Firefly Algorithm and Differential Evolution for Global Optimization

Application of Ant Colony Algorithm in Multi-objective Optimization Problems

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