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Multi-objective Particle Swarm Optimization with Integrated fireworks algorithm and Size Double Archiving

INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS 2025年第1期18卷 1-37页

作者： Zhang, Yansong Liu, Yanmin Zhang, Xiaoyan Song, Qian Ouyang, Aijia Yang, Jie Guizhou Univ Sch Math & Stat Guiyang 550025 Peoples R China Zunyi Normal Coll Zunyi 563002 Peoples R China Guizhou Minzu Univ Sch Data Sci & Informat Engn Guiyang 550025 Peoples R China

The multi-objective particle swarm optimization (MOPSO) is an optimization technique that mimics the foraging behavior of birds to solve difficult optimization problems. MOPSO is well known for its strong global search capability, which efficiently locates solutions that are close to the global optimum across a wide search domain. However, similar to many other optimization algorithms, the fast convergence property of MOPSO can occasionally lead to the population entering the local optimum too soon, obstructing researchers from investigating more efficient solutions. To address this challenge, the study proposes a novel framework that integrates the fireworks algorithm (FA) into MOPSO and establishes a size-double archiving mechanism to maintain population diversity. By preventing population homogenization, this mechanism promotes the retention of better solutions. Additionally, by fusing evolutionary data analysis with particle information, the study offers new individual optimal choices and adaptive parameter tuning to improve the algorithm's robustness and adaptability and better manage the complexity of multi-objective optimization problems (MOPs). The suggested algorithm is compared with several existing MOPSOs and multi-objective evolutionary algorithms (MOEAs) in simulation experiments. Standard test problems like ZDT, UF, and DTLZ are used in the experiments. The new algorithm performs exceptionally well in terms of improving convergence and population diversity, as well as demonstrating significant competitiveness for solving MOPs.

关键词： Multi-objective particle swarm optimization fireworks algorithm Double archiving Adaptive parameters

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FLAP: fireworks algorithm based probabilistic planning

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APPLIED SOFT COMPUTING 2025年 171卷

作者： Rao, Dongning Yi, Shanzhen Jiang, Zhihua Guangdong Univ Technol Sch Comp Guangzhou Guangdong Peoples R China Jinan Univ Dept Comp Sci Guangzhou Guangdong Peoples R China

Probabilistic planning based on the relational dynamic influence diagram language focuses on actions' concurrent and probabilistic effects that lead to an enormous search space. While the complexity of this problem makes it challenging for existing probabilistic planners to solve all outcomes deterministically within the time budget, parallel computing aims to tackle complex issues within a fixed time limit by facilitating more resources. Thus, motivated by evolutionary computing, we propose a novel planning approach, FLAP(fireworks algorithm based probAbilistic Planning), to improve the quality and the stability of probabilistic planning for the first time. FLAPschedules multiple planner clients and adapts the fireworks algorithm in which future states can be generated via simulating fireworks explosion, evaluated by the reward-aware fitness, and then assigned to different planners for the environment evolution. The search strategy of FLAPis a look-ahead enhanced anytime Monte Carlo Tree Search, where a tree structure is composed representing plan traces and a fitness function that captures traces' rewards is used to guide the growth of the tree. FLAPis implemented and evaluated on multiple competition domains and gains plan executions with higher rewards than the state-of-the-art planner. By sharing information among planners, not only the best execution of plans but also the small variance of all generated plans show the superiority of our approach.1

关键词： Probabilistic planning Relational dynamic-influence diagram language Evolutionary computing fireworks algorithm

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A New Hybrid algorithm Based on PSO and fireworks algorithm for Optimal Design of Metasurface Absorber in RF Energy Harvesting

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IEEE TRANSACTIONS ON MAGNETICS 2025年第2期61卷

作者： Chen, Na Yang, Shiyou An, Siguang Zhejiang Univ Coll Elect Engn Hangzhou 310027 Peoples R China China Jiliang Univ Dept Elect Engn Hangzhou 314423 Peoples R China

To address the nonlinear, multimodal, and computationally intensive challenges inherent in the optimal design of metasurface absorbers for radio frequency (RF) energy harvesting, we propose an innovative hybrid algorithm that combines particle swarm optimization (PSO) and fireworks algorithm (FWA). The proposed hybrid algorithm features several enhancements: 1) a quasi-scaling (Q.S.) mechanism, inspired by the frequency response of electromagnetic (EM) devices shifting with the scaling of physical parameters, is developed to balance the exploration and exploitation searches;2) a self-adaptive inertia weight is introduced to enhance the convergence speed;and 3) an elite-preservation strategy is employed to ensure a global convergence. The effectiveness and merits of the proposed algorithm are validated by solving first mathematical test functions;then, two practical cases: a multi-band absorber (1.8-3 GHz, 5.85 GHz) and a broadband absorber (8-15 GHz), where a new dual-polarized metasurface absorber topology is employed. Both cases are efficiently optimized, demonstrating high absorption efficiencies within the targeted bands, and validated by both simulated and measured results.

关键词： Convergence Sparks Explosions Radio frequency Metasurfaces Frequency response fireworks algorithm Standards Topology Particle swarm optimization Design optimization fireworks algorithm (FWA) hybrid algorithm metasurfaces particle swarm optimization (PSO)

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fireworks algorithm based on search space partition

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INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS 2022年第12期37卷 10783-10812页

作者： Li, Yifeng Tan, Ying Peking Univ Sch Artificial Intelligence Inst Artificial Intellignce Key Lab Machine Perceptron MOE Beijing 100871 Peoples R China

fireworks algorithm is a novel swarm intelligence optimization framework which focuses on the potential of collaboration among multiple subpopulations with independent search ability. Although it has been proved to perform excellently in many tasks, the collaborative mechanism of fireworks is still quite undeveloped. In this paper, a theoretical model of fireworks algorithm based on search space partition is proposed, analyzed and implemented. The local search of each firework is replaced by covariance matrix adaptation evolution strategy for efficient exploitation. A coordination strategy inspired from Voronoi diagram is proposed to approximate the theoretical model for stable global exploration. Experimental results show that the proposed algorithm not only outperforms previous variants of fireworks algorithm significantly, but also achieves competitive results compared with state-of-the-art evolutionary algorithms, which are intensively fine-tuned on the objective functions.

关键词： fireworks algorithm global optimization meta-heuristic search space partition swarm intelligence optimization algorithm

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fireworks algorithm framework for Big Data optimization

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MEMETIC COMPUTING 2016年第4期8卷 333-347页

作者： El Majdouli, Mohamed Amine Rbouh, Ismail Bougrine, Saad El Benani, Bouazza El Imrani, Abdelhakim Ameur Mohammed V Univ Fac Sci Concept & Syst Lab Rabat Morocco Mohammed V Univ Comp Sci Res Lab Fac Sci Rabat Morocco

This paper presents a novel optimization framework based on the fireworks algorithm for Big Data Optimization problems. Indeed, the proposed framework is composed of two optimization algorithms. A single objective fireworks algorithm and a multi-objective fireworks algorithm are proposed for solving the Big Optimization of Signals problem "Big-OPT" which belongs to the Big Data Optimization problems class. The single objective fireworks algorithm adopts a modified search mechanism to ensure rapidity and preserve the explorative capacities of the basic fireworks algorithm. Afterward, the algorithm is extended to handle multi-objective optimization of Big-OPT with a supplementary special sparks phase and a novel strategy for next generation selection. To validate the performance of the framework, extensive tests on six EEG datasets are performed. The framework is also compared with several approaches from recent state of the art. The study concludes the competitive performance of the proposed framework in comparison with the other techniques reported in this paper.

关键词： Big Data Optimization fireworks algorithm Big-OPT EEG Signals Brain Computer Interface

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fireworks algorithm for mean-VaR/CVaR models

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PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS 2017年 483卷 1-8页

作者： Zhang, Tingting Liu, Zhifeng Tianjin Univ Coll Management & Econ Tianjin 300072 Peoples R China Hainan Univ Sch Econ & Management Haikou 570228 Peoples R China

Intelligent algorithms have been widely applied to portfolio optimization problems. In this paper, we introduce a novel intelligent algorithm, named fireworks algorithm, to solve the mean-VaR/CVaR model for the first time. The results show that, compared with the classical genetic algorithm, fireworks algorithm not only improves the optimization accuracy and the optimization speed, but also makes the optimal solution more stable. We repeat our experiments at different confidence levels and different degrees of risk aversion, and the results are robust. It suggests that fireworks algorithm has more advantages than genetic algorithm in solving the portfolio optimization problem, and it is feasible and promising to apply it into this field. (C) 2017 Elsevier B.V. All rights reserved.

关键词： fireworks algorithm Portfolio optimization Genetic algorithm Value at risk

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fireworks algorithm with elitism-based selection strategy and optimal particle guidance mechanism

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2024年第3期46卷 7427-7446页

作者： Xing, Cheng Wang, Jie-Sheng Liu, Yu Univ Sci & Technol Liaoning Sch Elect & Informat Engn Anshan 114051 Peoples R China

With the increasing complexity and difficulty of numerical optimization problems in the real world, many efficient meta-heuristic optimization methods have been proposed to solve these problems. An improved fireworks algorithm (FWA) with elitism-based selection and optimal particle guidance strategies (EO-FWA) was proposed to address the limitations of the traditional FWA in terms of optimization accuracy and convergence speed, which not only improves the efficiency of the searching agent but also accelerates its convergence speed. In addition, by adopting boundary-based mapping rules, EO-FWA eliminates the randomness of traditional modulo operation mapping rules, which improves its stability and reliability. Twelve benchmark functions in CEC-BC-2022 are used to test the performance of EO-FWA, and the welded beam design problem is optimized at the end. The results show that EO-FWA exhibits stronger competitiveness than other algorithms in dealing with high-dimensional optimization problems and engineering optimization problem, and it can balance exploitation and exploration effectively so as to prevent the algorithm from falling into local optimal solutions.

关键词： fireworks algorithm elitist-based selection strategy optimal particle guidance optimization problem

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fireworks algorithm for RFID Network Planning Problem 25

Fireworks Algorithm for RFID Network Planning Problem

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25th International Conference RADIOELEKTRONIKA

作者： Tuba, Milan Bacanin, Nebojsa Beko, Marko Megatrend Univ Belgrade Grad Sch Comp Sci Bulevar Umetnosti 29 Belgrade 11070 Serbia Univ Lusofona Humanidades & Tecnol Dept Comp Engn Lisbon Portugal

ISBN: (纸本)9781479981175

This paper describes fireworks algorithm adjusted for solving multi-objective radio frequency identification (RFID) network planning problem. RFID network planning is a hard optimization problem that attracts research attention since the usage of the RFID technology expanded in many industries. Recent fireworks algorithm was successfully applied to other hard optimization problems. In our implementation of the fireworks algorithm for multi-objective RFID network planning problem we used hierarchical approach to objectives. For experimental purposes we used standard benchmark sets. A comparative analysis with other state-of-the-art metaheuristics proved that our proposed approach outperformed other algorithms. It was successful in achieving total coverage without interference with smaller number of deployed readers and less transmitted power.

关键词： RFID network planning fireworks algorithm swarm intelligence nature inspired algorithms constrained optimization

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fireworks algorithm Based Image Registration 7th

Fireworks Algorithm Based Image Registration

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7th International Workshop on Soft Computing Applications (SOFA)

作者： Bejinariu, Silviu-Ioan Costin, Hariton Rotaru, Florin Luca, Ramona Nita, Cristina Diana Lazar, Camelia Romanian Acad Inst Comp Sci Iasi Branch Iasi Romania Grigore T Popa Univ Med & Pharm Fac Med Bioengn Iasi Romania

ISBN: (纸本)9783319625218

In the Image Processing (IP) domain, optimization algorithms have to be applied in many cases. Nature-inspired heuristics allow obtaining near optimal solutions using lower computing resources. In this paper the fireworks algorithm (FWA) behavior is studied for Image Registration (IR) problems. The IR results accuracy is analyzed for different types of images, mainly in case of pixel based registration using the Normalized Mutual Information. FWA is compared to Particle Swarming (PSO), Cuckoo Search (CSA) and Genetic algorithms (GA) in terms of results accuracy and number of objective function evaluations required to obtain the optimal geometric transform parameters. Because the pixel based IR may fail in case of images containing graphic drawings, a features based IR approach is proposed for this class of images. Comparing to other nature inspired algorithms, FWA performances are close to those of PSO and CSA in terms of accuracy. Considering the required computing time, that is determined by the number of cost function evaluations, FWA is little slower than PSO and much faster than CSA and GA.

关键词： fireworks algorithm Optimization Image registration

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fireworks algorithm for Optimization 1

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1st International Conference on Swarm Intelligence

作者： Tan, Ying Zhu, Yuanchun Peking Univ Key Lab Machine Percept MOE Dept Machine Intelligence Beijing 100871 Peoples R China Peking Univ Sch Elect Engn & Comp Sci Beijing 100871 Peoples R China

ISBN: (数字)9783642134951

ISBN: (纸本)9783642134944

Inspired by observing fireworks explosion;a novel swarim intelligence algorithm, called fireworks algorithm (FA), is proposed for global optimization of complex functions in the proposed FA. two types of explosion (search) processes are employed, and the mechanisms for keeping diveisity of sparks rile also well designed In cadet to demon sit ate the validation or the FA;a number of experiments were conducted On mine benchmark test functions to compare the RA with two Variants of particle swami optimization (PSO) algorithms;namely Standard PSO and Clonal PSO It, fauns out, flow the results that the proposed FA clearly outperforms the two variants of the PSOs in both convergence speed and global solution accuracy

关键词： natural computing swarm metelligence fireworks algorithm particle swarm optimization function optimization

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