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Research on Firefly Algorithm Enhancement by Diversifying Swarm 8th

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Research on Firefly Algorithm Enhancement by Diversifying Sw...

8th International Conference on Cognitive Computing, ICCC 2024, Held as Part of the Services Conference Federation, SCF 2024

作者： Pan, Xiuqin Ren, Shuangqing School of Information Engineering Minzu University of China Beijing100081 China

ISBN: (纸本)9783031779534

In response to the issues faced by the traditional Firefly Algorithm (FA), particularly its tendency to become trapped in local optima and slow convergence during the global optimization process, especially for high-dimensional optimization problems, an improved version of the algorithm is proposed—Diverse Swarm Firefly Algorithm (DSFA). Enhancements to the algorithm are twofold: firstly, a new adaptive randomization parameter strategy is designed to meet the search requirements at different stages;secondly, a novel random search mechanism is introduced to enhance the diversity and quality of solutions during the iterative process. To validate the effectiveness of DSFA, simulation experiments were conducted on eight widely recognized benchmark test functions. The experimental results demonstrate that, compared to traditional intelligent optimization algorithms and another version of the improved Firefly Algorithm, DSFA exhibits significant advantages in both convergence speed and solution precision. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： optimization algorithms

High-dimensional efficient global optimization using both random and supervised embeddings

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arXiv 2025年

作者： Priem, Rémy Diouane, Youssef Bartoli, Nathalie Dubreuil, Sylvain Saves, Paul AIAA Journal

Bayesian optimization (BO) is one of the most powerful strategies to solvecomputationally expensive-to-evaluate blackbox optimization problems. However,BO methods are conventionally used for optimization problems of small dimensionbecause of the curse of dimensionality. In this paper, a high-dimensionnaloptimization method incorporating linear embedding subspaces of small dimensionis proposed to efficiently perform the optimization. An adaptive learningstrategy for these linear embeddings is carried out in conjunction with theoptimization. The resulting BO method, named efficient global optimizationcoupled with random and supervised embedding (EGORSE), combines in an adaptiveway both random and supervised linear embeddings. EGORSE has been compared tostate-of-the-art algorithms and tested on academic examples with a number ofdesign variables ranging from 10 to 600. The obtained results show the highpotential of EGORSE to solve high-dimensional blackbox optimization problems,in terms of both CPU time and the limited number of calls to the expensiveblackbox simulation. © 2025, CC BY-NC-ND.

关键词： optimization algorithms

A Centrality-Guided Modularity optimization Algorithm for Overlapping and Nested Community Detection in Opportunistic Networks 19th

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A Centrality-Guided Modularity Optimization Algorithm for Ov...

19th CCF Conference on Computer Supported Cooperative Work and Social Computing, ChineseCSCW 2024

作者： Wang, Shoucheng Guo, Xulong Xu, Gang Inner Mongolia University Hohhot010021 China Inner Mongolia Branch of National Computer Network Emergency Response Technical Team/Coordination Center of China Hohhot010021 China

ISBN: (纸本)9789819623723

This paper introduces a centrality-guided modularity optimization algorithm for overlapping and nested community detection (CG-MONCD), aimed at addressing community structure identification in opportunistic networks. Characterized by data transmission through sporadic encounters between nodes without the need for stable communication links, the CG-MONCD algorithm constructs a weighted graph model by quantifying the frequency and duration of encounters, as well as energy consumption. Incorporating a centrality-based initial community construction strategy and a method for identifying overlapping nodes, it effectively detects overlapping and nested communities within the network. Experimental results demonstrate that this algorithm outperforms existing methods in community detection, modularity optimization, and intra-community edge density, accurately revealing the network's nested communities and hierarchical features, thereby enhancing communication efficiency and network stability. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： optimization algorithms

METAFOR: A Hybrid Metaheuristics Software Framework for Single-Objective Continuous optimization Problems

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arXiv 2025年

作者： Camacho-Villalón, Christian Dorigo, Marco Stützle, Thomas Department of Knowledge Technologies Jožef Stefan Institute Ljubljana Slovenia Université Libre de Bruxelles Brussels Belgium

Hybrid metaheuristics are powerful techniques for solving difficult optimization problems that exploit the strengths of different approaches in a single implementation. For algorithm designers, however, creating hybrid metaheuristic implementations has become increasingly challenging due to the vast number of design options available in the literature and the fact that they often rely on their knowledge and intuition to come up with new algorithm designs. In this paper, we propose a modular metaheuristic software framework, called METAFOR, that can be coupled with an automatic algorithm configuration tool to automatically design hybrid metaheuristics. METAFOR is specifically designed to hybridize Particle Swarm optimization, Differential Evolution and Covariance Matrix Adaptation–Evolution Strategy, and includes a local search module that allows their execution to be interleaved with a subordinate local search. We use the configuration tool irace to automatically generate 17 different metaheuristic implementations and evaluate their performance on a diverse set of continuous optimization problems. Our results show that, across all the considered problem classes, automatically generated hybrid implementations are able to outperform configured single-approach implementations, while these latter offer advantages on specific classes of functions. We provide useful insights on the type of hybridization that works best for specific problem classes, the algorithm components that contribute to the performance of the algorithms, and the advantages and disadvantages of two well-known instance separation strategies, creating stratified training set using a fix percentage and leave-one-class-out cross-validation. © 2025, CC BY.

关键词： optimization algorithms

Modified FOX Optimizer for Solving optimization problems

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arXiv 2025年

作者： Hasan, Dler O. Mohammed, Hardi M. Abdul, Zrar Khalid Department of Computer Science College of Science Charmo University Chamchamal/Sulaimani Kurdistan Region46023 Iraq

The FOX optimizer, inspired by red fox hunting behavior, is a powerful algorithm for solving real-world and engineering problems. However, despite balancing exploration and exploitation, it can prematurely converge to local optima, as agent positions are updated solely based on the current best-known position, causing all agents to converge on one location. This study proposes the modified FOX optimizer (mFOX) to enhance exploration and balance exploration and exploitation in three steps. First, the Oppositional-Based Learning (OBL) strategy is used to improve the initial population. Second, control parameters are refined to achieve a better balance between exploration and exploitation. Third, a new update equation is introduced, allowing agents to adjust their positions relative to one another rather than relying solely on the best-known position. This approach improves exploration efficiency without adding complexity. The mFOX algorithm's performance is evaluated against 12 well-known algorithms on 23 classical benchmark functions, 10 CEC2019 functions, and 12 CEC2022 functions. It outperforms competitors in 74% of the classical benchmarks, 60% of the CEC2019 benchmarks, and 58% of the CEC2022 benchmarks. Additionally, mFOX effectively addresses four engineering problems. These results demonstrate mFOX's strong competitiveness in solving complex optimization tasks, including unimodal, constrained, and high-dimensional problems. © 2025, CC BY.

关键词： optimization algorithms

Pessimistic bilevel optimization approach for decision–focused learning

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arXiv 2025年

作者： Jiménez, Diego Pagnoncelli, Bernardo K. Yaman, Hande SKEMA Business School France Faculty of Economics and Business KU Leuven Belgium

The recent interest in contextual optimization problems, where randomness is associated with side information, has led to two primary strategies for formulation and solution. The first, estimate-then-optimize, separates the estimation of the problem’s parameters from the optimization process. The second, decision-focused optimization, integrates the optimization problem’s structure directly into the prediction procedure. In this work, we propose a pessimistic bilevel approach for solving general decision-focused formulations of combinatorial optimization problems. Our method solves an Ε-approximation of the pessimistic bilevel problem using a specialized cut generation algorithm. We benchmark its performance on the 0-1 knapsack problem against estimate-then-optimize and decision-focused methods, including the popular SPO+ approach. Computational experiments highlight the proposed method’s advantages, particularly in reducing out-of-sample *** Codes 90B99 Copyright © 2025, The Authors. All rights reserved.

关键词： optimization algorithms

optimization problems constrained by parameter sums

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arXiv 2025年

作者： Nash, John C. Varadhan, Ravi University of Ottawa Telfer School of Management OttawaON Canada Johns Hopkins University Johns Hopkins University Medical School BaltimoreMD United States

This article presents a discussion of optimization problems where the objective function f(x) has parameters that are constrained by some scaling, so that q(x) = constant, where this function q() involves a sum of the parameters, their squares, or similar simple function. Our focus is on ways to use standardized optimization programs to solve such problems rather than specialized codes. Examples are presented with *** Codes 49M99 © 2025, CC BY-NC-SA.

关键词： optimization algorithms

Research on Vehicle Collaborative Algorithm for Optimizing Intelligent Charging Pile of Electric Vehicles in the Internet of Vehicles Environment

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Research on Vehicle Collaborative Algorithm for Optimizing I...

2025 Asia-Europe Conference on Cybersecurity, Internet of Things and Soft Computing, CITSC 2025

作者： Wang, Jinsheng Dai, Jiarong Lv, Yuanyuan Zheng, Rui Chen, Fengyue Zhang, Xiaorong University of Engineering Shandong Vocational and Technical Shandong Jinan China

ISBN: (纸本)9798331504205

This paper proposes a collaborative optimization algorithm for intelligent charging piles of electric vehicles based on the Internet of Vehicles environment. The algorithm combines the real-time information interaction between vehicles to establish a charging demand prediction model, and designs an intelligent allocation strategy based on multi-objective optimization to improve the utilization efficiency of charging resources. Various influencing factors are considered, including the distribution density of charging piles, the driving trajectory of electric vehicles, and the spatiotemporal distribution of charging demand. In addition, the model simulation is carried out based on MATLAB, and the algorithm performance is analyzed through experimental data to verify the effectiveness of the proposed algorithm in optimizing the utilization rate of charging piles and shortening user waiting time. The simulation results show that compared with the traditional charging pile allocation method, the algorithm can significantly reduce the average waiting time (by about 25%) and improve the overall charging efficiency (by about 15%). Therefore, this study provides strong support for the optimization of charging resources for electric vehicles in the Internet of Vehicles environment, which is conducive to promoting the popularization of electric vehicles and the integrated application of Internet of Vehicles technology. © 2025 IEEE.

关键词： optimization algorithms

Research on Scheduling algorithms for Multi-UAV Collaborative Search 2nd

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Research on Scheduling Algorithms for Multi-UAV Collaborat...

2nd International Conference on the Frontiers of Robotics and Software Engineering, FRSE 2024

作者： Yu, Bocheng Tan, Qilin Zhang, Yang Wu, Na Xing, Liang Xi’an Aeronautics Computing Technique Research Institute AVIC Xi’an China

ISBN: (纸本)9789819630127

The unique wide-area surveillance capability of UAVs has led to their increasing use in environmental search tasks. This paper studies the use of SUAVs to assist UAVs in collaborative searches, aiming to expand the search area of UAVs while reducing search costs. Considering practical constraints such as UAV endurance, fuel consumption, and cost of detection sensor, a mathematical optimization model is constructed to minimize the whole costs. The mathematical problem is NP-hard, and an optimization algorithm is proposed to solve the problem. Simulation experiments demonstrate that the proposed algorithm can effectively reduce costs compared to conventional algorithms, and the computation time of the algorithm is significantly shorter than that of conventional algorithms. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： optimization algorithms