Thammano, Arit

Preferred name

Thammano, Arit

Alternative Name

Thammano, A.

Thummano, Arit

Main Affiliation

School of Information Technology

arit.th@kmitl.ac.th

Now showing 1 - 10 of 24

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Modified Ant System with Threshold for the Vehicle Routing Problem
(2022-01-01)
Rungwachira, Petcharat
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Thammano, Arit
Vehicle routing problem is a combinatorial optimization problem. Ant system, one of the most popular algorithms for solving the combinatorial optimization problem, is inspired by the ant foraging behavior. However, Ant system has the problem of premature convergence and easily getting trapped into local optimum. This paper proposes the modified ant system with threshold. In the beginning, the proposed algorithm randomly generates the initial population. To obtain a more diverse population, the transition probability and the threshold are used to determine which city should be next in the path. Moreover, the improved pheromone updating process is introduced to help reducing the rate of convergence. Three local searches, swap, insert, and reverse, are used in the proposed algorithm to prevent getting trapped in a local optimum. The datasets used in this research were taken from TSPLIB, the standard benchmark datasets for TSP. The performance of the algorithm was compared with that of the other 3 algorithms: GA-PSO-ACO, FOGS-ACO, and Hybrid VNS. The results on 20 datasets showed that the proposed method outperformed or at least at par with the others on 15 datasets. On the other hand, there was one dataset, rd100, on which the proposed algorithm was better than the best-known solution. According to the result, our modified ant system with threshold was very effective for the small- and medium-sized vehicle routing datasets.
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Hybrid Nature-Inspired Optimization Algorithm: Hydrozoan and Sea Turtle Foraging Algorithms for Solving Continuous Optimization Problems
(2020-01-01)
Tansui, Daranat
;
Thammano, Arit
In this paper, we develop a hybrid optimization algorithm inspired by the reproduction processes of hydrozoans and the foraging behavior of sea turtles for solving continuous optimization problems. Our hybrid algorithm combines the exploration capability of the hydrozoan algorithm with the exploitation capability of the sea turtle foraging algorithm. Moreover, a new adaptive crossover operator was introduced and integrated into the hybrid algorithm to further enhance exploration capability. Our hybrid algorithm was evaluated and compared to the individual algorithms and 12 state-of-the-art algorithms. Results on 21 standard benchmark functions showed that our algorithm was very effective and was among the best of the group, specifically it converged faster than the individual algorithms on most functions and reached optimal or near-optimal results on all functions.
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Differential Evolution for Classification: A Novel Classifier Technique in Data Mining
(2024-01-01)
Farda, Irfan
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Thammano, Arit
In the realm of Data Mining, the pursuit of innovative classification methodologies remains crucial for advancing robust techniques in handling complex and diverse datasets. This paper explores the application of Differential Evolution (DE), a powerful optimization algorithm, as a unique and effective optimization-based standalone classifier. Our exploration focused on harnessing DE's intrinsic capabilities, adapting it into a classifier while preserving its distinctiveness. The fundamental principle of DE for classification involves iteratively optimizing the center point for each class using DE operators, which are specialized mechanisms for exploring and refining solutions, and utilizing these optimized points for making predictions. The effectiveness of our model was evaluated on ten classification datasets from the UCI Machine Learning Repository and compared against three other classification methods: KNN, ZMP, and BPNN. Experimental results underscore the competitive performance of our proposed model, emphasizing the potential of DE in effectively addressing classification challenges.
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Solving Optimization Problems by a Hybrid Algorithm Based on Sand Cat Swarm Optimization and Invasive Weed Optimization Algorithm
(2025-01-01)
Pravesjit, Sakkayaphop
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Kantawong, Krittika
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Jitkongchuen, Duangjai
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Thammano, Arit
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Longpradit, Panchit
This paper addresses an optimization problem using hybrid algorithms of the Sand Cat Swarm Optimization (SCSO) and Invasive Weed Optimization (IWO). In this study, the reproduction step in the IWO algorithm was incorporated after the initial population step of the SCSO. The proposed algorithm was compared against the following: Intersection Mutation Differential Evolution (IMDE), Differential Evolution (DE), SCSO, and Whale Optimization Algorithm (WOA), whereby the performance was tested on six benchmark functions using a 10-fold cross validation. The results indicate that the proposed algorithm yielded the optimal solution for two out of the six benchmark functions. Additionally, when compared with the other four chosen algorithms, it yielded the best overall results. The findings suggest that the proposed algorithm is able to generate solutions similar to those obtained from the previous methods, essentially for the continuous step function, the multimodal function, and the discontinuous step function.
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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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Differential Evolution with Self-Adaptive Mutation and Population Improvement Strategy for Optimization Problems
(2024-01-01)
Farda, Irfan
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Thammano, Arit
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Morris, John
Differential Evolution (DE) algorithms are widely recognized as effective metaheuristic techniques used to solve continuous optimization problems. However, DE algorithms encounter limitations in exploration and exploitation due to their sensitivity to parameter settings and reliance on mutation strategies. To overcome these drawbacks, we introduced a self-adaptive mutation and population improvement strategy in differential evolution, SAMPIDE. The algorithm adjusts the mutation operator automatically according to the successful mutation operator value from the previous generation. Additionally, to enhance the algorithm's convergence, we adopted a random learning mechanism to update the population. SAMPIDE was compared with nine state-of-the-art DE variants and four other metaheuristic algorithms across thirty-two benchmark functions. SAMPIDE consistently outperformed others on unimodal, step and noisy functions, while maintaining competitive performance on multimodal functions. Additionally, when tested on five real-world problems, SAMPIDE outperformed other metaheuristic algorithms. Thus, the self-adaptive mutation and population improvement strategy incorporated into SAMPIDE positioned it as a promising solution for a wide range of optimization problems.
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A Novel Discrete Differential Evolution Algorithm for Solving the Traveling Salesman Problem
(2023-01-01)
Farda, Irfan
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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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An Adaptive Whale Optimization Algorithm with Mahalanobis Distance for Optimization Problems
(2022-01-01)
Jitkongchuen, Duangjai
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Sirikayon, Chaloemphon
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Thummano, Arit
This paper suggests using Mahalanobis distance to regenerate a new whale position to increase the performance of the whale optimization algorithm. Learning from previous evolutionary searches allows the probability parameters to be self-adapted. The suggested approach was compared to the classical whale optimization algorithm (WOA), particle swarm optimization (PSO), and differential evolution algorithm (DE) on 11 well-known benchmark functions. The results of the experiments showed that the proposed algorithm was effective in solving optimization problems.
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Swarm Intelligence Algorithms and Their Applications
(2024-09-01)
Tansui, Daranat
;
Thammano, Arit
Background and objectives: Algorithms inspired by the behavior of living organisms have been developed to apply principles from nature to solve problems in various aspects of daily life, e.g., in science, medicine, engineering, technology, and business management. The developed algorithms follow two main ideas. First, evolutionary algorithms use Darwin's principle of natural selection to choose the most suitable organisms in an unforgiving environment. Second, Swarm Intelligence Algorithms mimic the collaborative behavior of groups of living things, such as flocks of birds or schools of fish. The applications of these algorithms include medical data analysis for disease diagnosis, resource allocation planning in construction projects, traffic system design for improved safety, genetic data analysis to find relationships between genes and diseases, and business data analysis to predict customer buying behavior. These algorithms help increase efficiency, accuracy, and sustainability in solving various problems by applying principles inspired by nature. This article aims to explain working principles and performance, the advantages, disadvantages, and applications of Swarm Intelligence algorithms. Methodology: The main research steps were the following: 1. Studying and collecting research documents on 10 algorithms: Ant Colony Optimization, Artificial Bee Colony Algorithm, Bat Algorithm, Particle Swarm Optimization, Flower Pollination Algorithm, Weed Optimization Algorithm, Bee Colony Optimization, Cuckoo Search Algorithm, Bird Flocking Algorithm, and Chicken Swarm Optimization. 2. Analysis of working principles and efficiency of 10 algorithms. 3. Analyzing the advantages and disadvantages of the 10 algorithms. 4. Summarizing the applications of the 10 algorithms. 5. Research results and conclusions. Key findings: This article summarizes the applications of algorithms inspired by the behavior of living organisms to solve various daily life problems in fields such as science, medicine, engineering, technology, and business management. It also aims to explain the advantages and disadvantages of these algorithms in solving complex problems in various aspects of daily life, especially to efficiently achieve sustainable development in society and business. Some applications are such as the following: using intelligent algorithms in medical data analysis for disease diagnosis and environmental management; applying algorithms to analyze medical images for efficient diagnosis and treatment; utilizing algorithms in engineering and technology for infrastructure planning and design to enhance safety and operational efficiency; and using algorithms in business management for data analysis to predict customer behavior and marketing strategies. Implications: Swarm Intelligence algorithms, inspired by the collective behavior of living organisms, are valuable across various fields. In science, they can be utilized to locate underground oil or mineral resources through a network of sensors that collaborate to enhance efficiency in resource detection. In medicine, they aid in analyzing medical images for disease diagnosis and in complex surgeries by directing the movement of surgical robots, emulating precise, cooperative animal actions. In engineering, swarm algorithms improve intelligent traffic systems, simulating vehicle flows to alleviate congestion and plan safer routes. For technology, swarm-inspired collaborative robots excel in manufacturing, coordinating seamlessly with one another in intricate production lines. In agriculture, they are employed for managing smart farms and controlling drones for pest management. In business, swarm algorithms assist in analyzing consumer behavior by examining purchasing patterns, enabling accurate forecasting and adjustments to marketing strategies to better meet customer needs. Conclusions: This review found that Swarm Intelligence algorithms were that the 10 swarm intelligence algorithms have different working principles and performance when applied in various tasks, along with their own advantages and disadvantages and limitations in solving problems, with some algorithms being more effective in certain areas. This article also concludes that some types of algorithms can solve complex problems and can be applied in 5 different areas comprehensively. This review may be beneficial to algorithm developers and users alike.
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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.

Thammano, Arit

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