Abstract
A multi-objective optimization method for exploring uncharted space is presented in the study. Robotics exploration personnel create a map of the immediate area using sensor data. It has been observed that a single optimization technique is typically used with a specific objective function to carry out the optimization for space exploration. The optimization process can be sped up and simplified using a mono-objective function, but the map accuracy and exploration depth suffer. This work offers an optimization technique with improved multi-objective functions in recognition of this crucial factor. This not only speeds up the search procedure but also improves the precision of the maps. The Reconfigured Whale Algorithm (rWO), the suggested framework, is based on a Whale Optimizer inspired by whales’ biological behavior. Initializing the whale population, also known as waypoints, is the first step. After being established in the initial stage/iterations, these waypoints are considered constant. The location update from the robot-catered non-dominated waypoints is the next phase. The algorithm optimizes the waypoints. Extensive simulations that simulate various scenarios and environments are used to assess the performance matrices properly. Following the identification of the algorithm’s advantages, the effectiveness of the outcomes is demonstrated by contrasting its performance with that of three popular optimization algorithms: the hybrid CME-Whale Optimizer (WO), the Coordinated Multi-Robot Exploration (CME), and the Arithmetic Optimizer (AO) integrated with CME. Findings show that the suggested approach significantly improves the optimization process by expanding the area that is investigated and speeding up the search process.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Data availability
data is available from the authors upon reasonable request.
References
AbdelAziz AM, Soliman THA, Ghany KKA, Sewisy AAE-M (2019) A pareto-based hybrid whale optimization algorithm with tabu search for multi-objective optimization. Algorithms 12:261
Abualigah L, Elaziz MA, Khodadadi N, Forestiero A, Jia H, Gandomi AH (2022a) Aquila optimizer based pso swarm intelligence for iot task scheduling application in cloud computing. In: Integrating Meta-Heuristics and Machine Learning for Real-World Optimization Problems (pp. 481–497). Springer
Abualigah L, Forestiero A, Elaziz MA (2022b) Bio-inspired agents for a distributed nlp-based clustering in smart environments. In: Proceedings of the 13th International Conference on Soft Computing and Pattern Recognition (SoCPaR 2021) (pp. 678–687). Springer
Acharya S, Saha S (2018) Cancer tissue sample classification using point symmetry-based clustering algorithm. Int J Human Technol 1:19–39
Ahmed MM, Houssein EH, Hassanien AE, Taha A, Hassanien E (2019) Maximizing lifetime of large-scale wireless sensor networks using multi-objective whale optimization algorithm. Telecommun Syst 72:243–259
Alzu’bi, D., Abdullah, M., Hmeidi, I., AlAzab, R., Gharaibeh, M., El-Heis, M., Almotairi, K. H., Forestiero, A., Hussein, A. M., Abualigah, L. et al. (2022). Kidney tumor detection and classification based on deep learning approaches: A new dataset in ct scans. Journal of Healthcare Engineering, 2022
Burgard W, Moors M, Stachniss C, Schneider FE (2005) Coordinated multi-robot exploration. IEEE Trans Rob 21:376–386
Charrow, B., Liu, S., Kumar, V., & Michael, N. (2015). Information-theoretic mapping using cauchy-schwarz quadratic mutual information. In 2015 IEEE International Conference on Robotics and Automation (ICRA) (pp. 4791–4798). IEEE
Comito, C., Forestiero, A., & Zumpano, E. (2022). Integrating Artificial Intelligence and IoT for Advanced Health Informatics: AI in the Healthcare Sector. Springer Nature
Dahou, A., Abd Elaziz, M., Chelloug, S. A., Awadallah, M. A., Al-Betar, M. A., Al-qaness, M. A., & Forestiero, A. (2022). Intrusion detection system for iot based on deep learning and modified reptile search algorithm. Computational Intelligence and Neuroscience, 2022
Dai X, Long S, Zhang Z, Gong D (2019) Mobile robot path planning based on ant colony algorithm with a* heuristic method. Front Neurorobot 13:15
Dar JA, Srivastava KK, Lone SA (2022) Design and development of hybrid optimization enabled deep learning model for covid-19 detection with comparative analysis with dcnn, biat-gru, xgboost. Comput Biol Med 150:106123
Dar JA, Srivastava KK, Lone SA (2022) Spectral features and optimal hierarchical attention networks for pulmonary abnormality detection from the respiratory sound signals. Biomed Signal Process Control 78:103905
Ever YK (2017) Using simplified swarm optimization on path planning for intelligent mobile robot. Procedia computer science 120:83–90
Forestiero A (2022) Heuristic recommendation technique in internet of things featuring swarm intelligence approach. Expert Syst Appl 187:115904
Gul F, Mir I, Abualigah L, Mir S, Altalhi M (2022) Cooperative multi-function approach: A new strategy for autonomous ground robotics. Futur Gener Comput Syst 134:361–373
Gul F, Mir I, Abualigah L, Sumari P (2021) Multi-robot space exploration: An augmented arithmetic approach. IEEE Access 9:107738–107750
Gul F, Mir I, Rahiman W, Islam TU (2021) Novel implementation of multi-robot space exploration utilizing coordinated multi-robot exploration and frequency modified whale optimization algorithm. IEEE Access 9:22774–22787
Gul, F., Mir, S., & Mir, I. (2022b). Multi robot space exploration: A modified frequency whale optimization approach. In AIAA SCITECH 2022 Forum (p. 1416)
Gul F, Rahiman W, Alhady S, Ali A, Mir I, Jalil A (2021) Meta-heuristic approach for solving multi-objective path planning for autonomous guided robot using pso-gwo optimization algorithm with evolutionary programming. J Ambient Intell Humaniz Comput 12:7873–7890
Gul F, Rahiman W, Nazli Alhady SS (2019) A comprehensive study for robot navigation techniques. Cogent Engineering 6:1632046
Jiang J, Xin J (2019) Path planning of a mobile robot in a free-space environment using q-learning. Progress in artificial intelligence 8:133–142
Kumar, N., Vámossy, Z., & Szabó-Resch, Z. M. (2016). Robot path pursuit using probabilistic roadmap. In 2016 IEEE 17th International Symposium on Computational Intelligence and Informatics (CINTI) (pp. 000139–000144). IEEE
Kumawat, I. R., Nanda, S. J., & Maddila, R. K. (2017). Multi-objective whale optimization. In Tencon 2017-2017 ieee region 10 conference (pp. 2747–2752). IEEE
Mir I, Gul F, Mir S, Khan MA, Saeed N, Abualigah L, Abuhaija B, Gandomi AH (2022) A survey of trajectory planning techniques for autonomous systems. Electronics 11:2801
Mirjalili S, Lewis A (2016) The whale optimization algorithm. Adv Eng Softw 95:51–67
Mohammed HM, Umar SU, Rashid TA (2019) 2019. A systematic and meta-analysis survey of whale optimization algorithm, Computational intelligence and neuroscience
Siddiqi, F. A., & Rahman, C. M. (2018). Evolutionary multi-objective whale optimization algorithm. In International Conference on Intelligent Systems Design and Applications (pp. 431–446). Springer
Szczepanski, R., Bereit, A., & Tarczewski, T. (2021). Efficient local path planning algorithm using artificial potential field supported by augmented reality. Energies, 14. https://www.mdpi.com/1996-1073/14/20/6642. https://doi.org/10.3390/en14206642
Szczepanski, R., & Tarczewski, T. (2021). Global path planning for mobile robot based on artificial bee colony and dijkstra’s algorithms. In 2021 IEEE 19th International Power Electronics and Motion Control Conference (PEMC) (pp. 724–730). IEEE
Xin, B., Gao, G.-Q., Ding, Y.-L., Zhu, Y.-G., & Fang, H. (2017). Distributed multi-robot motion planning for cooperative multi-area coverage. In 2017 13th IEEE International Conference on Control & Automation (ICCA) (pp. 361–366). IEEE
Yamauchi, Y., Uehara, T., & Yamashita, M. (2016). Brief announcement: pattern formation problem for synchronous mobile robots in the three dimensional euclidean space. In Proceedings of the 2016 ACM Symposium on Principles of Distributed Computing (pp. 447–449)
Funding
This research received no external funding.
Author information
Authors and Affiliations
Corresponding authors
Ethics declarations
Conflict of interest
The authors declare that there is no conflict of interest regarding the publication of this paper.
Ethical approval
This article does not contain any studies with human participants or animals performed by any of the authors.
Informed consent
Informed consent was obtained from all individual participants included in the study.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Gul, F., Mir, I., Mir, S. et al. Multi-agent robotics system with whale optimizer as a multi-objective problem. J Ambient Intell Human Comput 14, 9637–9649 (2023). https://doi.org/10.1007/s12652-023-04636-3
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12652-023-04636-3