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Enhancing the firefly algorithm through a cooperative coevolutionary approach: an empirical study on benchmark optimisation problems

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INTERNATIONAL JOURNAL OF BIO-INSPIRED COMPUTATION 2014年第2期6卷 108-125页

作者： Trunfio, Giuseppe A. Univ Sassari Dept Architecture Planning & Design I-07041 Alghero SS Italy

In recent years, the firefly algorithm (FA) has been applied with success to many classes of optimisation problems. However, as is the case for all metaheuristic optimisation algorithms, also with FA can be observed a rapid deterioration of efficiency as the dimensionality of the search space increases. In this paper, we use a cooperative coevolutionary approach for enhancing FA with the aim of making it much more efficient in the case of search spaces with many dimensions. We assess the performance of the cooperative coevolutionary firefly algorithm (CCFA) through a computational study based on some significant benchmark functions with up to 1,000 dimensions. Moreover, we compare the proposed CCFA with two state-of-the-art algorithms for high-dimensional optimisation problems. According to our results, CCFA can lead to significantly improved solutions in comparison to the standard FA. In addition, we show that the CCFA computation time is significantly lower than that of FA.

关键词： cooperative coevolution large-scale optimisation swarm intelligence firefly algorithm bio-inspired computation

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An improved firefly algorithm for global continuous optimization problems

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EXPERT SYSTEMS WITH APPLICATIONS 2020年 149卷 113340-000页

作者： Wu, Jinran Wang, You-Gan Burrage, Kevin Tian, Yu-Chu Lawson, Brodie Ding, Zhe Queensland Univ Technol Sch Math Sci Brisbane Qld Australia Univ Oxford Dept Comp Sci Oxford England Queensland Univ Technol Sch Comp Sci Brisbane Qld Australia

Global continuous optimization is populated by its implementation in many real-world applications. Such optimization problems are often solved by nature-inspired and meta-heuristic algorithms, including the firefly algorithm (FA), which offers fast exploration and exploitation. To further strengthen FA's search for global optimum, a Levy-flight FA (LF-FA) has been developed through sampling from a Levy distribution instead of the traditional uniform one. However, due to its poor exploitation in local areas, the LF-FA does not guarantee fast convergence. To address this problem, this paper provides an adaptive logarithmic spiral-Levy FA (AD-IFA) that strengthens the LF-FA's local exploitation and accelerates its convergence. Our AD-IFA is integrated with logarithmic-spiral guidance to its fireflies' paths, and adaptive switching between exploration and exploitation modes during the search process. Experimental results show that the AD-IFA presented in this paper consistently outperforms the standard FA and LF-FA for 29 test functions and 6 real cases of global optimization problems in terms of both computation speed and derived optimum. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Adaptive switch Logarithmic spiral firefly algorithm Global continuous optimization

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Load frequency controller design of a two-area system composing of PV grid and thermal generator via firefly algorithm

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NEURAL COMPUTING & APPLICATIONS 2018年第2期30卷 607-616页

作者： Abd-Elazim, S. M. Ali, E. S. Zagazig Univ Elect Power & Machine Dept Fac Engn Zagazig Egypt

In this paper, firefly algorithm (FA) for optimal tuning of PI controllers for load frequency control of hybrid system composing of photovoltaic (PV) system and thermal generator is introduced. Also, maximum power point tracking of PV is considered in the design process. The block diagram of the hybrid system is performed. To robustly tune the parameters of controllers, a time-domain-based objective function is established which is solved by the FA. Simulation results are presented to show the improved performance of the suggested FA-based controllers compared with genetic algorithm (GA). These results show that the proposed controllers present better performance over GA in terms of settling times and different indices.

关键词： firefly algorithm LFC PI controller Two-area system PV grid MPPT

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Hybrid Particle Swarm Optimization-firefly algorithm (HPSOFF) for combinatorial optimization of non-slicing VLSI floorplanning

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2017年第1期32卷 661-669页

作者： Sivaranjani, P. Kumar, A. Senthil Kongu Engn Coll Dept Elect & Commun Engn Erode Tamil Nadu India Dr Mahalingam Coll Engn & Technol Dept Elect & Elect Engn Pollachi Tamil Nadu India

Floorplanning is the initial step in the process of designing layout of the chip. It is employed to plan the positions and shapes of modules during the process of VLSI Design cycle to optimize the cost metrics like layout area and wirelength. In this paper, a Hybrid Particle Swarm Optimization-firefly (HPSOFF) algorithm is proposed which integrates Particle Swarm Optimization (PSO), firefly (FF) and Modified Corner List (MCL) algorithms. Initially, PSO algorithm utilizes MCL algorithm for non-slicing floorplan representations and fitness value evaluation. The solutions obtained from PSO are provided as initial solutions to FF algorithm. Fitness function evaluation and floorplan representations for FF algorithm are again carried out using MCL algorithm. The proposed algorithm is illustrated using Microelectronics Centre of North Carolina (MCNC) and Gigascale Systems Research Centre (GSRC) benchmark circuits. The results obtained are compared with the solutions derived from other stochastic algorithms and the proposed algorithm provides better solutions for both the benchmark circuits.

关键词： VLSI floorplanning Particle Swarm Optimization algorithm firefly algorithm Modified Corner List (MCL) algorithm Hybrid Particle Swarm Optimization-firefly algorithm (HPSOFF) non-slicing floorplan

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Optimal groundwater remediation design of pump and treat systems via a simulation-optimization approach and firefly algorithm

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ENGINEERING OPTIMIZATION 2015年第1期47卷 1-17页

作者： Kazemzadeh-Parsi, Mohammad Javad Daneshmand, Farhang Ahmadfard, Mohammad Amin Adamowski, Jan Martel, Richard Islamic Azad Univ Shiraz Branch Dept Mech Engn Shiraz Iran McGill Univ Fac Agr & Environm Sci Dept Bioresource Engn Ste Anne De Bellevue PQ Canada McGill Univ Dept Mech Engn Montreal PQ Canada Univ Quebec Inst Natl Rech Sci Ctr Eau Terre & Environm Quebec City PQ Canada

In the present study, an optimization approach based on the firefly algorithm (FA) is combined with a finite element simulation method (FEM) to determine the optimum design of pump and treat remediation systems. Three multi-objective functions in which pumping rate and clean-up time are design variables are considered and the proposed FA-FEM model is used to minimize operating costs, total pumping volumes and total pumping rates in three scenarios while meeting water quality requirements. The groundwater lift and contaminant concentration are also minimized through the optimization process. The obtained results show the applicability of the FA in conjunction with the FEM for the optimal design of groundwater remediation systems. The performance of the FA is also compared with the genetic algorithm (GA) and the FA is found to have a better convergence rate than the GA.

关键词： multi-objective optimization remediation design groundwater firefly algorithm finite element method

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A Linear Antenna Array Failure Correction with Null Steering using firefly algorithm

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DEFENCE SCIENCE JOURNAL 2014年第2期64卷 136-142页

作者： Grewal, N. S. Rattan, Munish Patterh, Manjeet Singh Guru Nanak Dev Engn Coll Ludhiana 141006 Punjab India Punjabi Univ Univ Coll Engn Patiala 147002 Punjab India

The element failure of digital beam forming array antenna systems used in defence equipment increases the side lobe power level which distorts the beam pattern of the antenna array. The problem of array failure correction becomes more complex when null steering conditions are required to be added. In this paper, the problem of linear antenna array failure has been addressed with multiple wide band null steering using firefly algorithm (FA) by controlling the amplitude and phase excitation of array elements. A fitness function in the form of template has been formulated to obtain the error between original (pre-failed) side lobe pattern and measured side lobe pattern and this error function has been minimized using FA. Numerical example of element failure correction of element failure of array along with multiple nulls is presented to show the capability of this flexible approach.

关键词： Digital beamforming array antenna system linear antenna array side lobe level element failure null steering optimization firefly algorithm

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Adaptive firefly algorithm with chaos for mechanical design optimization problems

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APPLIED SOFT COMPUTING 2015年 36卷 152-164页

作者： Baykasoglu, Adil Ozsoydan, Fehmi Burcin Dokuz Eylul Univ Fac Engn Dept Ind Engn TR-35397 Izmir Turkey

firefly algorithm (FA) is a newer member of bio-inspired meta-heuristics, which was originally proposed to find solutions to continuous optimization problems. Popularity of FA has increased recently due to its effectiveness in handling various optimization problems. To enhance the performance of the FA even further, an adaptive FA is proposed in this paper to solve mechanical design optimization problems, and the adaptivity is focused on the search mechanism and adaptive parameter settings. Moreover, chaotic maps are also embedded into AFA for performance improvement. It is shown through experimental tests that some of the best known results are improved by the proposed algorithm. (C) 2015 Elsevier B.V. All rights reserved.

关键词： firefly algorithm Chaotic maps Design optimization Non-linear programming

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Optimization of electrical discharge machining parameters on hardened die steel using firefly algorithm

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ENGINEERING WITH COMPUTERS 2015年第1期31卷 1-9页

作者： Raja, S. Bharathi Pramod, C. V. Srinivas Krishna, K. Vamshee Ragunathan, Arvind Vinesh, Somalaraju SASTRA Univ Sch Mech Engn Thanjavur 613401 Tamil Nadu India

Electric discharge machining (EDM) is one of the most widely used die-making processes especially in aerospace, automobile and electronics industries. The profile manufactured by EDM process should be dimensionally and geometrically accurate apart from good finish. This expectation is very much important as the die manufactured from EDM process is subjected to subsequent mass production. The material normally selected for die making will be superior in quality and hence time and cost of production will also be high. Selection of optimum EDM parameters may reduce the machining time along with maintaining required surface finish and dimensional accuracy. So there is a need to develop a technique for selecting the optimal EDM parameters to achieve the desired performance measures. In the present work, a recently developed firefly algorithm (FA) was implemented in the developed mathematical model based on the experiments conducted on an EDM. Investigations are also carried out to study the effect of EDM parameters such as current and pulse-on time on the surface roughness and machining time. The optimized machining parameters and the developed empirical relations are validated by confirmatory experiments. Machining parameters limits and desired surface finish are considered as practical constraints for both experimental and theoretical approaches. The predicted and actual machining time and surface roughness values reveals that FA is very much suitable for solving machining parameters optimization problems.

关键词： Electric discharge machining Machining parameters Machining time Surface roughness firefly algorithm

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Optimization of reinforced concrete retaining walls via hybrid firefly algorithm with upper bound strategy

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KSCE JOURNAL OF CIVIL ENGINEERING 2016年第6期20卷 2428-2438页

作者： Sheikholeslami, Razi Khalili, Behnam Gholipour Sadollah, Ali Kim, JoongHoon Univ Saskatchewan Sch Environm & Sustainabil Saskatoon SK Canada Univ Tehran Dept Engn Kish Int Campus Tehran Iran Korea Univ Sch Civil Environm & Architectural Engn Seoul 02841 South Korea

This paper represents a novel hybrid optimization method that uses an improved firefly algorithm with a harmony search algorithm (IFA-HS), for optimizing the cost of reinforced concrete retaining walls. The IFA-HS is utilized to find an economical design adhering to ACI 318-05 provisions. Two design examples regarding retaining walls are optimized using the proposed hybrid method, and the optimization results confirm the validity and efficiency of the developed algorithm. The IFA-HS method offers improvements on the recently developed firefly algorithm. These improvements include utilizing the memory that contains information extracted online during a search, employing pitch adjusting operation of HS during firefly updates, and modifying the movement phase of the FA. Moreover, to decrease the computational effort of the IFA-HS, the upper bound strategy, which is a recently developed strategy for reducing the total number of structural analyses, is incorporated during the optimization process.

关键词： concrete retaining wall metaheuristics firefly algorithm harmony search hybrid optimization

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IFA-EO: An improved firefly algorithm hybridized with extremal optimization for continuous unconstrained optimization problems

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SOFT COMPUTING 2023年第6期27卷 2943-2964页

作者： Chen, Min-Rong Yang, Liu-Qing Zeng, Guo-Qiang Lu, Kang-Di Huang, Yi-Yuan South China Normal Univ Sch Comp Sci Guangzhou 510631 Peoples R China Guangzhou Coll Commerce Sch Informat Technol & Engn Guangzhou 511363 Peoples R China Wenzhou Univ Natl Local Joint Engn Lab Digitalize Elect Design Wenzhou 325035 Peoples R China Jinan Univ Coll Cyber Secur Guangzhou 510632 Peoples R China Zhejiang Univ Inst Cyber Syst & Control Hangzhou 310018 Peoples R China

As one of the evolutionary algorithms, firefly algorithm (FA) has been widely used to solve various complex optimization problems. However, FA has significant drawbacks in slow convergence rate and is easily trapped into local optimum. To tackle these defects, this paper proposes an improved FA combined with extremal optimization (EO), named IFA-EO, where three strategies are incorporated. First, to balance the tradeoff between exploration ability and exploitation ability, we adopt a new attraction model for FA operation, which combines the full attraction model and the single attraction model through the probability choice strategy. In the single attraction model, small probability accepts the worse solution to improve the diversity of the offspring. Second, the adaptive step size is proposed based on the number of iterations to dynamically adjust the attention to the exploration model or exploitation model. Third, we combine an EO algorithm with powerful ability in local-search into FA. Experiments are tested on two group popular benchmarks including complex unimodal and multimodal functions. Our experimental results demonstrate that the proposed IFA-EO algorithm can deal with various complex optimization problems and has similar or better performance than the other eight FA variants, three EO-based algorithms, and one advanced differential evolution variant in terms of accuracy and statistical results.

关键词： firefly algorithm extremal optimization Probability choice strategy Adaptive step size Continuous optimization problems

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