Abstract
Brain Storm Optimization (BSO) is a swarm intelligence algorithm that mimics the brainstorming process of human beings. Since the introduction of BSO, many attempts have been made to improve the performance of the algorithm, mainly with respect to its convergence time and quality of solutions. In this work, we introduce a novel modified version of BSO named Stepladder Determinative BSO (S-DBSO). This algorithm is inspired by a brainstorming technique for decision making, proposed in the field of psychology. This is called Stepladder Technique, and it considers a creative brainstorming process that guarantees the equal participation of all members, even the most introverted of the group, to express their thoughts. The main achievements of this study include: 1) computational modeling of the Stepladder brainstorming process for algorithmic optimization; 2) optimization of the search ability of the algorithm towards the best solutions, after having given the right directionality to the newly generated individuals; 3) better convergence speed with fewer iterations of algorithm required, compared to other state-of-the-art methods and 4) avoidance of being trapped into local optima. Experiments based on well-recognized benchmarks, including the test suite of the competition for single-objective real parameter optimization of the Institute of Electrical and Electronics Engineers Congress on Evolutionary Computation (IEEE CEC 2017), validate that S-DBSO outperforms the original and state-of-the-art variations of BSO, as well as other state-of-the-art metaheuristic algorithms, including Particle Swarm Optimization (PSO) and Bat Algorithm (BA).
Similar content being viewed by others
Availability of data and material (data transparency)
The test suite of the competition for single-objective real parameter optimization of the 2017 IEEE Congress on Evolutionary Computation (IEEE CEC 2017) is used [68], which is available at https://www3.ntu.edu.sg/home/epnsugan/.
Code availability
Not applicable.
References
Yadav A, Vishwakarma DK (2020) A comparative study on bio-inspired algorithms for sentiment analysis. Clust Comput 23(4):2969–2989
Hosseinabadi AAR, Tirkolaee EB (2018) A gravitational emulation local search algorithm for task scheduling in multi-agent system. Int J Appl Opt Stud 1(01):11–24
Nasir M, Sadollah A, Choi YH, Kim JH (2020) A comprehensive review on water cycle algorithm and its applications. Neural Comput & Applic 32(23):17433–17488
Hatamlou A (2013) Black hole: a new heuristic optimization approach for data clustering. Inf Sci 222:175–184
Islam MR, Saifullah CK, Mahmud MR (2019) Chemical reaction optimization: survey on variants. Evol Intel 12(3):395–420
Kaveh A, Khanzadi M, and Moghaddam MR, (2020),“Billiards-inspired optimization algorithm; a new meta-heuristic method,” in Structures 27, pp. 1722–1739
Fan X, Sayers W, Zhang S, Han Z, Ren L, Chizari H (2020) Review and classification of bio-inspired algorithms and their applications. J Bionic Eng 17:611–631
Whitley D (1994) A genetic algorithm tutorial. Stat Comput 4(2):65–85
Price KV, (2013) “Differential evolution,” in Handbook of optimization, Springer, pp. 187–214
Nenavath H, Jatoth RK (2018) Hybridizing sine cosine algorithm with differential evolution for global optimization and object tracking. Appl Soft Comput 62:1019–1043
Yildizdan G, Baykan ÖK (2020) A novel modified bat algorithm hybridizing by differential evolution algorithm. Expert Syst Appl 141:112949
Luan J, Yao Z, Zhao F, Song X (2019) A novel method to solve supplier selection problem: hybrid algorithm of genetic algorithm and ant colony optimization. Math Comput Simul 156:294–309
Parsopoulos KE and Vrahatis MN, (2010) “Particle swarm optimization and intelligence: advances and applications”
Xu G, Cui Q, Shi X, Ge H, Zhan Z-H, Lee HP, Liang Y, Tai R, Wu C (2019) Particle swarm optimization based on dimensional learning strategy. Swarm Evol Comp 45:33–51
Cheng R, Jin Y (2014) A competitive swarm optimizer for large scale optimization. IEEE Trans Cybern 45(2):191–204
Gandomi AH, Alavi AH (2012) Krill herd: a new bio-inspired optimization algorithm. Commun Nonlinear Sci Numer Simul 17(12):4831–4845
Mirjalili S, Lewis A (2016) The whale optimization algorithm. Adv Eng Softw 95:51–67
Tubishat M, Abushariah MA, Idris N, Aljarah I (2019) Improved whale optimization algorithm for feature selection in Arabic sentiment analysis. Appl Intell 49(5):1688–1707
Shayanfar H, Gharehchopogh FS (2018) Farmland fertility: a new metaheuristic algorithm for solving continuous optimization problems. Appl Soft Comput 71:728–746
Zhao W, Zhang Z, Wang L (2020) Manta ray foraging optimization: an effective bio-inspired optimizer for engineering applications. Eng Appl Artif Intell 87:103300
Kamboj VK, Nandi A, Bhadoria A, Sehgal S (2020) An intensify Harris hawks optimizer for numerical and engineering optimization problems. Appl Soft Comput 89:106018
Abdullah JM, Ahmed T (2019) Fitness dependent optimizer: inspired by the bee swarming reproductive process. IEEE Access 7:43473–43486
Li S, Chen H, Wang M, Heidari AA, Mirjalili S (2020) Slime mould algorithm: a new method for stochastic optimization. Futur Gener Comput Syst 111:300–323
Sulaiman MH, Mustaffa Z, Saari MM, Daniyal H (2020) Barnacles mating optimizer: a new bio-inspired algorithm for solving engineering optimization problems. Eng Appl Artif Intell 87:103330
Yapici H, Cetinkaya N (2019) A new meta-heuristic optimizer: pathfinder algorithm. Appl Soft Comput 78:545–568
Cao Y, Wang Q, Wang Z, Jermsittiparsert K, Shafiee M (2020) A new optimized configuration for capacity and operation improvement of CCHP system based on developed owl search algorithm. Energy Rep 6:315–324
de Vasconcelos Segundo EH, Mariani VC, dos Santos Coelho L (2019) Design of heat exchangers using falcon optimization algorithm. Appl Therm Eng 156:119–144
Pierezan J and Coelho LDS, (2018) “Coyote optimization algorithm: a new metaheuristic for global optimization problems,” in 2018 IEEE congress on evolutionary computation (CEC), pp. 1–8
Houssein EH, Saad MR, Hashim FA, Shaban H, Hassaballah M (2020) Lévy flight distribution: a new metaheuristic algorithm for solving engineering optimization problems. Eng Appl Artif Intell 94:103731
Civicioglu P (2012) Transforming geocentric cartesian coordinates to geodetic coordinates by using differential search algorithm. Comput Geosci 46:229–247
Kashan AH (2014) League championship algorithm (LCA): an algorithm for global optimization inspired by sport championships. Appl Soft Comput 16:171–200
Atashpaz-Gargari E and Lucas C, (2007) “Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition,” in 2007 IEEE congress on evolutionary computation, pp. 4661–4667
Zhao W, Wang L, Zhang Z (2019) Supply-demand-based optimization: a novel economics-inspired algorithm for global optimization. IEEE Access 7:73182–73206
Das B, Mukherjee V, Das D (2020) Student psychology based optimization algorithm: a new population based optimization algorithm for solving optimization problems. Adv Eng Softw 146:102804
Moosavi SHS, Bardsiri VK (2019) Poor and rich optimization algorithm: a new human-based and multi populations algorithm. Eng Appl Artif Intell 86:165–181
Shabani A, Asgarian B, Salido M, Gharebaghi SA (2020) Search and rescue optimization algorithm: a new optimization method for solving constrained engineering optimization problems. Expert Syst Appl 161:113698
Singh A, Sharma S, Singh J (2021) Nature-inspired algorithms for wireless sensor networks: a comprehensive survey. Comp Sci Rev 39:100342
Yilmaz AE and Weber G-W, (2011) “Why you should consider nature-inspired optimization methods in financial mathematics,” in Nonlinear and Complex Dynamics, Springer, pp. 241–255
Shi Y, (2011) “Brain storm optimization algorithm,” in International conference in swarm intelligence, pp. 303–309
Cheng S, Qin Q, Chen J, Shi Y (2016) Brain storm optimization algorithm: a review. Artif Intell Rev 46(4):445–458
Tuba E, Dolicanin E, and Tuba M, (2017)“Chaotic brain storm optimization algorithm,” in International Conference on Intelligent Data Engineering and Automated Learning, pp. 551–559
Dai C, Lei X (2019) A multiobjective brain storm optimization algorithm based on decomposition. Complexity 2019:11
Sun Y, Wei J, Wu T, Xiao K, Bao J, Jin Y (2020) Brain storm optimization using a slight relaxation selection and multi-population based creating ideas ensemble. Appl Intell 50:3137–3161
Ma L, Zhang T, Wang R, Yang G, and Zhang Y, (2019) “Pbar: Parallelized brain storm optimization for association rule mining,” in 2019 IEEE Congress on Evolutionary Computation (CEC), pp. 1148–1156
Guo Y, Yang H, Chen M, Gong D, Cheng S (2020) Grid-based dynamic robust multi-objective brain storm optimization algorithm. Soft Comput 24(10):7395–7415
Cervantes-Castillo A, Mezura-Montes E (2020) A modified brain storm optimization algorithm with a special operator to solve constrained optimization problems. Appl Intell 50(12):4145–4161
Cao Z, Wang L (2019) An active learning brain storm optimization algorithm with a dynamically changing cluster cycle for global optimization. Clust Comput 22(4):1413–1429
Yu Y, Gao S, Wang Y, Lei Z, Cheng J, Todo Y (2019) A multiple diversity-driven brain storm optimization algorithm with adaptive parameters. IEEE Access 7:126871–126888
Liu J, Peng H, Wu Z, Chen J, Deng C (2020) Multi-strategy brain storm optimization algorithm with dynamic parameters adjustment. Appl Intell 50(4):1289–1315
Sovatzidi G and Iakovidis DK, (2020) “Determinative Brain Storm Optimization,” in International Conference on Swarm Intelligence, pp. 259–271
Yan X, Zhu Z, Wu Q, Gong W, Wang L (2019) Elastic parameter inversion problem based on brain storm optimization algorithm. Memetic Comp 11(2):143–153
Zhang W, Zhang Y, Peng C (2019) Brain storm optimization for feature selection using new individual clustering and updating mechanism. Appl Intell 49(12):4294–4302
Revathi ST, Ramaraj N, Chithra S (2019) Brain storm-based whale optimization algorithm for privacy-protected data publishing in cloud computing. Clust Comput 22(2):3521–3530
Yadav P (2019) Cluster based-image descriptors and fractional hybrid optimization for medical image retrieval. Clust Comput 22(1):1345–1359
Sovatzidi G, Savelonas M, Koutsiou D-CC, and Iakovidis DK, (2020) “Image Segmentation based on Determinative Brain Storm optimization,” in 2020 15th International Workshop on Semantic and Social Media Adaptation and Personalization SMA, pp. 1–6
Gang L, Yongli Z, Wei J (2020) Dynamic economic emission dispatch with wind power based on improved multi-objective brain storm optimisation algorithm. IET Renew Power Gener 14(13):2526–2537
Kanmani M, Narasimhan V (2020) Optimal fusion aided face recognition from visible and thermal face images. Multimed Tools Appl 79(25):17859–17883
Yang J, Shen Y, Shi Y (2020) Visual fixation prediction with incomplete attention map based on brain storm optimization. Appl Soft Comput 96:106653
Rogelberg SG, Barnes-Farrell JL, Lowe CA (1992) The stepladder technique: an alternative group structure facilitating effective group decision making. J Appl Psychol 77(5):730
Osborn AF (1953) Applied imagination. Scribner’s. Charles Scribner, New York
Sutton RI, Hargadon A (1996) Brainstorming groups in context: effectiveness in a product design firm. Adm Sci Q 41:685–718
Diehl M, Stroebe W (1987) Productivity loss in brainstorming groups: toward the solution of a riddle. J Pers Soc Psychol 53(3):497
Michinov N, Morice J, Ferrières V (2015) A step further in peer instruction: using the stepladder technique to improve learning. Comput Educ 91:1–13
Orpen C (1997) Using the stepladder technique to improve team performance. Psychol Stud
Segaran T, (2007) Programming collective intelligence: building smart web 2.0 applications. O"Reilly Media, Inc.
Carrasco J, Garc𝚤a S, Rueda M, Das S, Herrera F (2020) Recent trends in the use of statistical tests for comparing swarm and evolutionary computing algorithms: practical guidelines and a critical review. Swarm Evol Comp 54:100665
Holm S (1979) A simple sequentially rejective multiple test procedure. Scand J Stat 6:65–70
Awad NH, Ali MZ, Liang BYQJJ, Suganthan PN (2016) Problem Definitions and Evaluation Criteria for the CEC 2017 Special Session and Competition on Single Objective Bound Constrained Real-Parameter Numerical Optimization. Tech Rep, Nanyang Technological University, Singapore. [online] Available: https://www3.ntu.edu.sg/home/epnsugan/
Jamil M, Yang X-S (2013) A literature survey of benchmark functions for global optimisation problems. Int J Math Model Numer Optim 4(2):150–194
Field A, (2013) Discovering statistics using IBM SPSS statistics. Sage
Funding
This research has been co-financed by the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship and Innovation, under the call RESEARCH—CREATE—INNOVATE (project code: T1EDK-02070).
Author information
Authors and Affiliations
Contributions
Not applicable.
Corresponding author
Ethics declarations
Conflicts of interest/competing interests
Not applicable.
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Sovatzidi, G., Iakovidis, D.K. Stepladder determinative brain storm optimization. Appl Intell 52, 16799–16817 (2022). https://doi.org/10.1007/s10489-022-03171-6
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10489-022-03171-6