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Modified Bat Algorithm: a newly proposed approach for solving complex and real-world problems

  • Optimization
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Abstract

Bat Algorithm (BA) is a nature-inspired metaheuristic search algorithm designed to efficiently explore complex problem spaces and find near-optimal solutions. The algorithm is inspired by the echolocation behavior of bats, which acts as a signal system to estimate the distance and hunt prey. Although the BA has proven effective for various optimization problems, it exhibits limited exploration ability and susceptibility to local optima. The algorithm updates velocities and positions based on the current global best solution, causing all agents to converge toward a specific location, potentially leading to local optima issues in optimization problems. On this premise, this paper proposes the Modified Bat Algorithm (MBA) as an enhancement to address the local optima limitation observed in the original BA. MBA incorporates the frequency and velocity of the current best solution, enhancing convergence speed to the optimal solution and preventing local optima entrapment. While the original BA faces diversity issues, both the original BA and MBA are introduced. To assess MBA’s performance, three sets of test functions (classical benchmark functions, CEC2005, and CEC2019) are employed, with results compared to those of the original BA, Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Dragonfly Algorithm (DA). The outcomes demonstrate the MBA’s significant superiority over other algorithms. In addition, MBA successfully addresses a real-world assignment problem (call center problem), traditionally solved using linear programming methods, with satisfactory results.

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References

  • Abdulkhaleq MT, Rashid TA, Alsadoon A et al (2022) Harmony search: current studies and uses on healthcare systems. Artif Intell Med 131:102348

    Google Scholar 

  • Abualigah L, Shehab M, Alshinwan M, Alabool H (2020) Salp swarm algorithm: a comprehensive survey. Neural Comput Appl 32:11195–11215

    Google Scholar 

  • Abualigah L, Diabat A, Mirjalili S et al (2021a) The arithmetic optimization algorithm. Comput Methods Appl Mech Eng 376:113609

    MathSciNet  Google Scholar 

  • Abualigah L, Yousri D, Abd Elaziz M et al (2021b) Aquila optimizer: a novel meta-heuristic optimization algorithm. Comput Ind Eng 157:107250

    Google Scholar 

  • Abualigah L, Abd Elaziz M, Sumari P et al (2022) Reptile Search Algorithm (RSA): a nature-inspired meta-heuristic optimizer. Expert Syst Appl 191:116158

    Google Scholar 

  • Adnan RM, Mostafa RR, Kisi O et al (2021) Improving streamflow prediction using a new hybrid ELM model combined with hybrid particle swarm optimization and grey wolf optimization. Knowl Based Syst 230:107379

    Google Scholar 

  • Adnan RM, Kisi O, Mostafa RR et al (2022) The potential of a novel support vector machine trained with modified mayfly optimization algorithm for streamflow prediction. Hydrol Sci J 67:161–174

    Google Scholar 

  • Adnan RM, Mostafa RR, Dai H-L et al (2023) Pan evaporation estimation by relevance vector machine tuned with new metaheuristic algorithms using limited climatic data. Eng Appl Comput Fluid Mech 17:2192258

    Google Scholar 

  • Agushaka JO, Ezugwu AE, Abualigah L (2022) Dwarf mongoose optimization algorithm. Comput Methods Appl Mech Eng 391:114570

    MathSciNet  Google Scholar 

  • Ahmed AM, Rashid TA, Saeed SAM (2020) Cat swarm optimization algorithm: a survey and performance evaluation. Comput Intell Neurosci. https://doi.org/10.1155/2020/4854895

    Article  Google Scholar 

  • Ahmed AM, Rashid TA, Saeed SAM (2021) Dynamic Cat Swarm Optimization algorithm for backboard wiring problem. Neural Comput Appl 33:13981–13997

    Google Scholar 

  • Alhijawi B, Awajan A (2023) Genetic algorithms: theory, genetic operators, solutions, and applications. Evol Intell. https://doi.org/10.1007/s12065-023-00822-6

    Article  Google Scholar 

  • Alsalibi B, Abualigah L, Khader AT (2021) A novel bat algorithm with dynamic membrane structure for optimization problems. Appl Intell 51:1992–2017

    Google Scholar 

  • Boudjemaa R, Oliva D, Ouaar F (2020) Fractional Lévy flight bat algorithm for global optimisation. Int J Bio-Inspir Comput 15:100–112

    Google Scholar 

  • Cui L, Li G, Wang X et al (2017a) A ranking-based adaptive artificial bee colony algorithm for global numerical optimization. Inf Sci (n Y) 417:169–185

    Google Scholar 

  • Cui L, Li G, Zhu Z et al (2017b) A novel artificial bee colony algorithm with an adaptive population size for numerical function optimization. Inf Sci (n Y) 414:53–67

    MathSciNet  Google Scholar 

  • Cui Z, Li F, Zhang W (2019) Bat algorithm with principal component analysis. Int J Mach Learn Cybern 10:603–622

    Google Scholar 

  • Daş GS, Gzara F, Stützle T (2020) A review on airport gate assignment problems: single versus multi objective approaches. Omega (westport) 92:102146

    Google Scholar 

  • Delahaye D, Chaimatanan S, Mongeau M (2019) Simulated annealing: from basics to applications. In: Gendreau M, Potvin J-Y (eds) Handbook of metaheuristics. Springer International Publishing, Cham, pp 1–35

    Google Scholar 

  • Dey N (2020) Applications of firefly algorithm and its variants. Springer

    Google Scholar 

  • Fister I, Rauter S, Yang X-S et al (2015) Planning the sports training sessions with the bat algorithm. Neurocomputing 149:993–1002

    Google Scholar 

  • Gelareh S, Glover F, Guemri O et al (2020) A comparative study of formulations for a cross-dock door assignment problem. Omega (westport) 91:102015

    Google Scholar 

  • Ghanem WAHM, Jantan A (2018) Hybridizing artificial bee colony with monarch butterfly optimization for numerical optimization problems. Neural Comput Appl 30:163–181

    Google Scholar 

  • Gupta D, Agrawal U, Arora J, Khanna A (2020) Bat-inspired algorithm for feature selection and white blood cell classification. Nature-inspired computation and swarm intelligence. Elsevier, pp 179–197

    Google Scholar 

  • Houssein EH, Younan M, Hassanien AE (2019) Nature-inspired algorithms: a comprehensive review. In: Bhattacharyya S, Snášel V, Pan I, De D, Bhattacharyya S, Snášel V, Pan I, De D (eds) Hybrid computational intelligence. CRC Press, pp 1–25

    Google Scholar 

  • Houssein EH, Gad AG, Hussain K, Suganthan PN (2021) Major advances in particle swarm optimization: theory, analysis, and application. Swarm Evol Comput 63:100868

    Google Scholar 

  • Ikram RMA, Dai H-L, Al-Bahrani M, Mamlooki M (2022a) Prediction of the FRP reinforced concrete beam shear capacity by using ELM-CRFOA. Measurement 205:112230

    Google Scholar 

  • Ikram RMA, Dai H-L, Ewees AA et al (2022b) Application of improved version of multi verse optimizer algorithm for modeling solar radiation. Energy Rep 8:12063–12080

    Google Scholar 

  • Ikram RMA, Ewees AA, Parmar KS et al (2022c) The viability of extended marine predators algorithm-based artificial neural networks for streamflow prediction. Appl Soft Comput 131:109739

    Google Scholar 

  • Kadkhodazadeh M, Farzin S (2022) A novel hybrid framework based on the ANFIS, discrete wavelet transform, and optimization algorithm for the estimation of water quality parameters. J Water Clim Change 13:2940–2961

    Google Scholar 

  • Khishe M, Mosavi MR (2020) Chimp optimization algorithm. Expert Syst Appl 149:113338

    Google Scholar 

  • Kiełkowicz K, Grela D (2016) Modified bat algorithm for nonlinear optimization. Int J Comput Sci Netw Secur (IJCSNS) 16:46–50

    Google Scholar 

  • Kumar Y, Kaur A (2021) Variants of bat algorithm for solving partitional clustering problems. Eng Comput 38:1–27

    Google Scholar 

  • Kumbhkar A, Garg D, Lamba S, Pingolia M (2020) Variants of cuckoo search with levy flight and Dynamic Strategy Based Cuckoo Search (DSBCS). Second international conference on computer networks and communication technologies: ICCNCT 2019. Springer, Cham, pp 787–796

    Google Scholar 

  • Liang J-J, Qu BY, Gong DW, Yue CT (2019) Problem definitions and evaluation criteria for the CEC 2019 special session on multimodal multiobjective optimization. Zhengzhou University, Computational Intelligence Laboratory

    Google Scholar 

  • Liu R, Li S, Yang L (2020) Collaborative optimization for metro train scheduling and train connections combined with passenger flow control strategy. Omega (westport) 90:101990

    Google Scholar 

  • Mirjalili S, Lewis A (2016) The whale optimization algorithm. Adv Eng Softw 95:51–67

    Google Scholar 

  • Mohammed HM, Umar SU, Rashid TA (2019) A systematic and meta-analysis survey of whale optimization algorithm. Comput Intell Neurosci. https://doi.org/10.1155/2019/8718571

    Article  Google Scholar 

  • Oyelade ON, Ezugwu AE-S, Mohamed TIA, Abualigah L (2022) Ebola optimization search algorithm: a new nature-inspired metaheuristic optimization algorithm. IEEE Access 10:16150–16177

    Google Scholar 

  • Öztürk Ş, Ahmad R, Akhtar N (2020) Variants of Artificial Bee Colony algorithm and its applications in medical image processing. Appl Soft Comput 97:106799

    Google Scholar 

  • Pant M, Zaheer H, Garcia-Hernandez L, Abraham A (2020) Differential evolution: a review of more than two decades of research. Eng Appl Artif Intell 90:103479

    Google Scholar 

  • Peres F, Castelli M (2021) Combinatorial optimization problems and metaheuristics: review, challenges, design, and development. Appl Sci 11:6449

    Google Scholar 

  • Perwaiz U, Younas I, Anwar AA (2020) Many-objective BAT algorithm. PLoS ONE 15:e0234625

    Google Scholar 

  • Rahman CM, Rashid TA (2019) Dragonfly algorithm and its applications in applied science survey. Comput Intell Neurosci. https://doi.org/10.1155/2019/9293617

    Article  Google Scholar 

  • Rajasekhar A, Lynn N, Das S, Suganthan PN (2017) Computing with the collective intelligence of honey bees–a survey. Swarm Evol Comput 32:25–48

    Google Scholar 

  • Shami TM, El-Saleh AA, Alswaitti M et al (2022) Particle swarm optimization: a comprehensive survey. IEEE Access 10:10031–10061

    Google Scholar 

  • Shehab M, Abu-Hashem MA, Shambour MKY et al (2023) A comprehensive review of bat inspired algorithm: variants, applications, and hybridization. Arch Comput Methods Eng 30:765–797

    Google Scholar 

  • Ustun D, Carbas S, Toktas A (2021) Multi-objective optimization of engineering design problems through pareto-based bat algorithm. Applications of bat algorithm and its variants. Springer Singapore, Singapore, pp 19–43

    Google Scholar 

  • Yang X-S (2010a) Nature-inspired metaheuristic algorithms. Luniver press

  • Yang X-S (2010b) A new metaheuristic bat-inspired algorithm. Nature inspired cooperative strategies for optimization (NICSO 2010) 65–74

  • Yılmaz S, Kucuksille EU, Cengiz Y (2014) Modified bat algorithm. ElAEE. https://doi.org/10.5755/j01.eee.20.2.4762

    Article  Google Scholar 

  • Yuan X, Chen C, Lei X et al (2018) Monthly runoff forecasting based on LSTM–ALO model. Stoch Env Res Risk Assess 32:2199–2212

    Google Scholar 

Download references

Acknowledgements

Different universities provided the facilities and ongoing assistance necessary to carry out this work, which the authors sincerely appreciate.

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Authors and Affiliations

  1. Department of Software, College of Computer Science and Information Technology, University of Kirkuk, Kirkuk, Iraq

    Shahla U. Umar & Mohammed Rashad Baker

  2. Computer Science and Engineering Department, School of Science and Engineering, University of Kurdistan Hewler, KRI, Erbil, Iraq

    Tarik A. Rashid, Aram M. Ahmed & Bryar A. Hassan

  3. Department of Computer Science, College of Science, Charmo University, Chamchamal, Sulaimani, 46023, Iraq

    Bryar A. Hassan

Authors
  1. Shahla U. Umar
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  2. Tarik A. Rashid
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  3. Aram M. Ahmed
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  5. Mohammed Rashad Baker
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Correspondence to Shahla U. Umar.

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Umar, S.U., Rashid, T.A., Ahmed, A.M. et al. Modified Bat Algorithm: a newly proposed approach for solving complex and real-world problems. Soft Comput 28, 7983–7998 (2024). https://doi.org/10.1007/s00500-024-09761-5

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