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Subject: search.filters.subject.Firefly Algorithm

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    A HEURISTIC ENHANCING ARTIFICIAL IMMUNE SYSTEM FOR THREE-DIMENSIONAL LOADING CAPACITATED VEHICLE ROUTING PROBLEM
    (2025-06-08)
    Thapatsuwan, Peeraya
    ;
    Thapatsuwan, Warattapop
    ;
    Kulworatit, Chaichana
    This study addresses the Three-Dimensional Loading Capacitated Vehicle Routing Problem (3L-CVRP), a highly complex NP-hard problem that combines vehicle routing with spatially constrained three-dimensional bin packing. To tackle this challenge, we propose an enhanced Artificial Immune System (En-AIS) that integrates a novel local search heuristic called “Bring-i-to-j,” designed to improve routing feasibility and loading efficiency. The En-AIS algorithm is further refined through rigorous parameter tuning using a full factorial design and ANOVA analysis. Comparative experiments were conducted against conventional AIS and the Firefly Algorithm (FA) across 27 benchmark instances. Results demonstrate that En-AIS consistently outperforms both baseline methods in terms of solution quality, achieving an average improvement of 15–20% while maintaining competitive computational times. These findings highlight the algorithm’s robustness and its practical potential for application in logistics and supply chain optimization tasks involving joint routing and loading decisions.
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    Feature Selection Method Based on Hybrid Cuckoo Search and Firefly Algorithm for Breast Cancer Prediction
    (2025-01-01)
    Teerasarn, Chalanwich
    ;
    Kimpan, Warangkhana
    This research focuses on developing an efficient feature selection process using a hybrid technique combining Cuckoo Search algorithm and Firefly Algorithm. The Wisconsin Diagnostic Breast Cancer dataset is utilized to evaluate the capability of selecting significant features and eliminating irrelevant ones. The experimental results demonstrate that using the hybrid technique significantly improves the accuracy of machine learning compared to using the Cuckoo Search and Firefly Algorithm individually. Additionally, an analysis was conducted on the impact of splitting the dataset for training and testing, with splits of 70/30, 80/20, and 90/10. The experiments revealed a relationship between the number of selected features and the model accuracy. The findings from this study can serve as a guideline for developing appropriate feature selection process for complex data analysis problems.
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    Optimum Operation and Cost Scenarios of a Hybrid Wind/PV/Battery in a Radial Network using Firefly Algorithm and Surrogate Optimization
    (2022-01-01)
    Abdelwareth, Mohamed Els S.
    ;
    Riawan, Dedet Candra
    ;
    Chompoo-Inwai, Chow
    Exploiting renewable energy resources can help decrease carbon emissions by providing a reliable solution to generate electricity and tackle the climate change dilemma. Artificial Intelligence algorithms have been used in the last two decades to optimize power system networks. In this paper, we discussed the effect of replacing the existing Diesel Generator (DG) with a wind turbine to satisfy the load in the standalone hybrid (DG, PV, Battery) radial network in Tomia Island, south-east Sulawesi, Indonesia. Loss of Power Supply Probability (LPSP) and the Coefficient of determination parameters were used as technical performance indicators. Firefly Algorithm (FF) and Surrogate Optimization technique were used to optimize the system considering the minimum costs.
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    A new selection operator to improve the performance of genetic algorithm for optimization problems
    (2013-11-25)
    Ritthipakdee, Amarita
    ;
    Thammano, Arit
    ;
    Premasathian, Nol
    ;
    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.