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An Improved Differential Evolution Algorithm for Numerical Optimization Problems
(2023-06-01)
Farda, Irfan
;
Thammano, Arit
The differential evolution algorithm has gained popularity for solving complex optimization problems because of its simplicity and efficiency. However, it has several drawbacks, such as a slow convergence rate, high sensitivity to the values of control parameters, and the ease of getting trapped in local optima. In order to overcome these drawbacks, this paper integrates three novel strategies into the original differential evolution. First, a population improvement strategy based on a multi-level sampling mechanism is used to accelerate convergence and increase the diversity of the population. Second, a new self-adaptive mutation strategy balances the exploration and exploitation abilities of the algorithm by dynamically determining an appropriate value of the mutation parameters; this improves the search ability and helps the algorithm escape from local optima when it gets stuck. Third, a new selection strategy guides the search to avoid local optima. Twelve benchmark functions of different characteristics are used to validate the performance of the proposed algorithm. The experimental results show that the proposed algorithm performs significantly better than the original DE in terms of the ability to locate the global optimum, convergence speed, and scalability. In addition, the proposed algorithm is able to find the global optimal solutions on 8 out of 12 benchmark functions, while 7 other well-established metaheuristic algorithms, namely NBOLDE, ODE, DE, SaDE, JADE, PSO, and GA, can obtain only 6, 2, 1, 1, 1, 1, and 1 functions, respectively.
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Enhancing Genetic Algorithm Performance with Hybrid Strategy for Solving Optimization Problems
(2023-01-01)
Farda, Irfan
;
Thammano, Arit
This study presents a novel algorithm called hybrid-GA, which combines genetic algorithm (GA) with the Harris Hawks Optimization (HHO) algorithm to address the challenge of enhancing GA performance in solving optimization problems. While GA is known for its strong exploration capabilities, it often faces challenges in exploitation, limiting its ability to find global optimal solutions. The hybrid-GA algorithm aims to surpass existing methods by achieving a better balance between exploration and exploitation, resulting in improved solution quality, faster convergence, and enhanced exploration-exploitation ability. The algorithm effectiveness is demonstrated through experiments on six benchmark functions from CEC2017, where the hybrid-GA outperforms compared algorithms on five of six functions, showcasing its potential for enhancing GA performance in optimization problem-solving. These findings contribute to advancing the field by providing a promising solution to address the exploration-exploitation challenge in GA-based optimization.
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A Novel Discrete Differential Evolution Algorithm for Solving the Traveling Salesman Problem
(2023-01-01)
Farda, Irfan
;
Thammano, Arit
This research introduces a novel approach, Discrete Differential Evolution (DDE), to address the Traveling Salesman Problem (TSP), a classic combinatorial optimization challenge with wide-ranging real-world applications. While initially designed for continuous optimization problems, the Differential Evolution algorithm has been adapted to suit the discrete nature of the TSP. Our approach involves several key modifications to the standard Differential Evolution algorithm. These include the creation of mutant vectors through random subtour optimization, the utilization of order crossover to generate trial vectors, and the application of a pool tournament population selection method to identify the most promising candidates. To assess the effectiveness of our proposed algorithm, we conducted comprehensive computational experiments across multiple TSP instances. Comparative analyses against other algorithms, such as the Hybrid Differential Evolution (HDE) and Discrete Particle Swarm Optimization (DPSO), consistently demonstrated that our proposed DDE outperformed the competitors on all Traveling Salesman test instances. This underscores the algorithm's superior performance, as evidenced by its consistently superior results in both the best and the mean solution quality, along with lower Relative Error (RE) values, signifying its proficiency in identifying routes closer to the optimum.
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A Self-adaptive Differential Evolution Algorithm for Solving Optimization Problems
(2022-01-01)
Farda, Irfan
;
Thammano, Arit
This research proposes a novel self-adaptive differential evolution algorithm for solving continuous optimization problems. This paper focuses on redesiging the self-adaptive strategy for the mutation parameters. The new mutation parameters adjust themselves to the current situation of the algorithm. When the search is stagnant, the first mutation parameter that scales the difference between the best vector and the target vector will be increased. In contrast, the second mutation parameter that scales the difference between two random target vectors will be decreased. On the other hand, when the search progresses well towards the global optimum, the algorithm will enhance the search of the surrounding space by doing the opposite of the above actions. The performance of the proposed self-adaptive differential evolution algorithm was evaluated and compared with the classic differential evolution algorithm on 7 benchmark functions. The experimental results showed that the proposed algorithm converged much faster than the classic differential evolution algorithm on all benchmark functions.
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Primate swarm algorithm for continuous optimization problems
(2017-08-29)
Ritthipakdee, Amarita
;
Thammano, Arit
In primate life, there are a number of various social behavior, such as communication among members in a group, and food sharing, which are vital to maintain their survival. Similar to those of Swarm Intelligence, such as ant colony optimization, the behavior of primates motivates us to develop an algorithm with the aim of solving continuous problems. Our algorithm is inspired by the behavior of the primate. The communication among them is studied and is also a key part in their food finding strategy. Our proposed algorithm developed upon the behavior is tested with twelve standard benchmark functions and most of which converged to the optimal value.
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A self-adaptive differential evolution algorithm for continuous optimization problems
(2014-09-01)
Jitkongchuen, Duangjai
;
Thammano, Arit
This paper proposes a new self-adaptive differential evolution algorithm (DE) for continuous optimization problems. The proposed self-adaptive differential evolution algorithm extends the concept of the DE/current-to-best/1 mutation strategy to allow the adaptation of the mutation parameters. The control parameters in the mutation operation are gradually self-adapted according to the feedback from the evolutionary search. Moreover, the proposed differential evolution algorithm also consists of a new local search based on the krill herd algorithm. In this study, the proposed algorithm has been evaluated and compared with the traditional DE algorithm and two other adaptive DE algorithms. The experimental results on 21 benchmark problems show that the proposed algorithm is very effective in solving complex optimization problems.
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A new selection operator to improve the performance of genetic algorithm for optimization problems
(2013-11-25)
Ritthipakdee, Amarita
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Thammano, Arit
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Premasathian, Nol
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Uyyanonvara, Bunyarit
Nature-inspired algorithms, such as Particle swarm optimization (PSO), Ant colony optimization (ACO), and Firefly algorithm, are well known for solving NP-hard optimization problems. They are capable of obtaining optimal solutions in a reasonable time. The algorithm presented in this paper is a combination of a firefly mating concept and genetic algorithm. Genetic algorithm is used as the core of the algorithm while a firefly mating concept is used to compose a new selection operator. The proposed algorithm is tested on four standard benchmark functions. Experimental results have confirmed that the proposed algorithm is not only computationally more efficient than both the original firefly algorithm and the genetic algorithm but also almost always ensure the optimal solutions. © 2013 IEEE.
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A novel optimization algorithm based on the natural behavior of the ant colonies
(2013-01-01)
Kiatwuthiamorn, Jiraporn
;
Thammano, Arit
Optimization problem is one of the most challenging problems that has received considerable attention over the last decade. Many metaheuristic methods have been proposed and successfully applied to find the optimal solution. Each technique has its good and bad points. A new optimization technique based on the natural behavior of the ant colonies is proposed in this paper. In this proposed algorithm, the foraging behavior of worker ants is employed for locally searching for better solution while the marriage, breeding, and feeding behaviors are used in reproduction of the new generation. The proposed algorithm has been evaluated on several benchmark problems. The experimental results demonstrate the effectiveness of the proposed algorithm. © 2013 The Authors. Published by Elsevier B.V.

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