Partial Leader Optimizer

Purba Daru Kusuma, Faisal Candrasyah Hasibuan


A new swarm intelligence-based metaheuristic optimizer, namely Partial Leader Optimizer (PLO), is presented. PLO contains several autonomous agents that represent the solution. The best solution represents collective intelligence, i.e., the leader. PLO has distinct mechanics in finding the acceptable solution during the given iteration. Every agent moves to a specified target in every iteration. Two options can be chosen to determine the target. First, the target is calculated by pushing the virtual best solution away from the corresponding agent. Second, the target is randomly chosen within the solution space. This target selection is conducted stochastically based on the threshold that is set manually before the iteration. Then, several candidates are generated between the target and the agent's current location. The distance between adjacent candidates is the same. The agent moves to the best candidate and updates the best solution. Simulation is implemented to observe and analyze the PLO’s performance. The well-known 23 benchmark functions are used as the optimization problems. In this simulation, PLO is benchmarked with marine predator algorithm (MPA), particle swarm optimization (PSO), average subtraction-based optimizer (ASBO), slime mold algorithm (SMA), and pelican optimization algorithm (POA). The result shows that PLO is competitive compared to these algorithms, especially in solving fixed-dimension multimodal functions. PLO is better than PSO, MPA, SMA, ASBO, and POA in optimizing 22, 19, 18, 9, and 20 functions out of 23, respectively.


Metaheuristic; swarm intelligence; quantitative optimization

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W. Wu, W. Zhou, Y. Lin, Y. Xie, and W. Jin, “A hybrid metaheuristic algorithm for location inventory routing problem with time windows and fuel consumption,” Expert Syst Appl, vol. 166, p. 114034, Mar. 2021, doi: 10.1016/j.eswa.2020.114034.

H. H. Miyata and M. S. Nagano, “An iterated greedy algorithm for distributed blocking flow shop with setup times and maintenance operations to minimize makespan,” Comput Ind Eng, vol. 171, p. 108366, Sep. 2022, doi: 10.1016/j.cie.2022.108366.

M. R. Othman, Z. Ali Othman, A. I. Srour, and N. S. Sani, “A Hybrid Water Flow-Like Algorithm and Variable Neighbourhood Search for Traveling Salesman Problem,” Int J Adv Sci Eng Inf Technol, vol. 9, no. 5, p. 1505, Oct. 2019, doi: 10.18517/ijaseit.9.5.7957.

A. M. Fathollahi-Fard, A. Ahmadi, and B. Karimi, “Multi-Objective Optimization of Home Healthcare with Working-Time Balancing and Care Continuity,” Sustainability, vol. 13, no. 22, p. 12431, Nov. 2021, doi: 10.3390/su132212431.

M. Mokhtari, M. Vaziri Sarashk, M. Asadpour, N. Saeidi, and O. Boyer, “Developing a Model for the University Course Timetabling Problem: A Case Study,” Complexity, vol. 2021, pp. 1–12, Dec. 2021, doi: 10.1155/2021/9940866.

X. Shi, “A Method of Optimizing Network Topology Structure Combining Viterbi Algorithm and Bayesian Algorithm,” Wirel Commun Mob Comput, vol. 2021, pp. 1–12, May 2021, doi: 10.1155/2021/5513349.

J. Zhao, M. Ye, Z. Yang, Z. Xing, and Z. Zhang, “Operation optimizing for minimizing passenger travel time cost and operating cost with time-dependent demand and skip-stop patterns: Nonlinear integer programming model with linear constraints,” Transp Res Interdiscip Perspect, vol. 9, p. 100309, Mar. 2021, doi: 10.1016/j.trip.2021.100309.

J. Swan et al., “Metaheuristics ‘In the Large,’” Eur J Oper Res, vol. 297, no. 2, pp. 393–406, Mar. 2022, doi: 10.1016/j.ejor.2021.05.042.

M. Dehghani, S. Hubalovsky, and P. Trojovsky, “Northern Goshawk Optimization: A New Swarm-Based Algorithm for Solving Optimization Problems,” IEEE Access, vol. 9, pp. 162059–162080, 2021, doi: 10.1109/ACCESS.2021.3133286.

A. Faramarzi, M. Heidarinejad, S. Mirjalili, and A. H. Gandomi, “Marine Predators Algorithm: A nature-inspired metaheuristic,” Expert Syst Appl, vol. 152, p. 113377, Aug. 2020, doi: 10.1016/j.eswa.2020.113377.

S. Mirjalili, S. M. Mirjalili, and A. Lewis, “Grey Wolf Optimizer,” Advances in Engineering Software, vol. 69, pp. 46–61, Mar. 2014, doi: 10.1016/j.advengsoft.2013.12.007.

S. Suyanto, A. A. Ariyanto, and A. F. Ariyanto, “Komodo Mlipir Algorithm,” Appl Soft Comput, vol. 114, p. 108043, Jan. 2022, doi: 10.1016/j.asoc.2021.108043.

A. M. Fathollahi-Fard, M. Hajiaghaei-Keshteli, and R. Tavakkoli-Moghaddam, “Red deer algorithm (RDA): a new nature-inspired meta-heuristic,” Soft comput, vol. 24, no. 19, pp. 14637–14665, Oct. 2020, doi: 10.1007/s00500-020-04812-z.

M. Noroozi, H. Mohammadi, E. Efatinasab, A. Lashgari, M. Eslami, and B. Khan, “Golden Search Optimization Algorithm,” IEEE Access, vol. 10, pp. 37515–37532, 2022, doi: 10.1109/ACCESS.2022.3162853.

P. Trojovský and M. Dehghani, “Pelican Optimization Algorithm: A Novel Nature-Inspired Algorithm for Engineering Applications,” Sensors, vol. 22, no. 3, p. 855, Jan. 2022, doi: 10.3390/s22030855.

M. Dehghani and P. Trojovský, “Hybrid leader based optimization: a new stochastic optimization algorithm for solving optimization applications,” Sci Rep, vol. 12, no. 1, p. 5549, Dec. 2022, doi: 10.1038/s41598-022-09514-0.

F. Zeidabadi, M. Dehghani, and O. Malik, “TIMBO: Three Influential Members Based Optimizer,” International Journal of Intelligent Engineering and Systems, vol. 14, no. 5, pp. 121–128, Oct. 2021, doi: 10.22266/ijies2021.1031.12.

S. Kaur, L. K. Awasthi, A. L. Sangal, and G. Dhiman, “Tunicate Swarm Algorithm: A new bio-inspired based metaheuristic paradigm for global optimization,” Eng Appl Artif Intell, vol. 90, p. 103541, Apr. 2020, doi: 10.1016/j.engappai.2020.103541.

M. Suman, V. Sakthivel, and P. Sathya, “Squirrel Search Optimizer: Nature Inspired Metaheuristic Strategy for Solving Disparate Economic Dispatch Problems,” International Journal of Intelligent Engineering and Systems, vol. 13, no. 5, pp. 111–121, Oct. 2020, doi: 10.22266/ijies2020.1031.11.

S. Arora and S. Singh, “Butterfly optimization algorithm: a novel approach for global optimization,” Soft comput, vol. 23, no. 3, pp. 715–734, Feb. 2019, doi: 10.1007/s00500-018-3102-4.

M. Dehghani et al., “MLO: Multi Leader Optimizer,” International Journal of Intelligent Engineering and Systems, vol. 13, no. 6, pp. 364–373, Dec. 2020, doi: 10.22266/ijies2020.1231.32.

B. Liu, Z. Wang, and X. Zhong, “Particle Swarm Optimization Algorithm in Numerical Simulation of Saturated Rock Slope Slip,” Math Probl Eng, vol. 2021, pp. 1–11, Mar. 2021, doi: 10.1155/2021/6682659.

T. Gao, Q. Tang, J. Li, Y. Zhang, Y. Li, and J. Zhang, “A Particle Swarm Optimization With Lévy Flight for Service Caching and Task Offloading in Edge-Cloud Computing,” IEEE Access, vol. 10, pp. 76636–76647, 2022, doi: 10.1109/ACCESS.2022.3192846.

H. Mohammed Hussein, K. Katzis, L. P. Mfupe, and E. T. Bekele, “Performance Optimization of High-Altitude Platform Wireless Communication Network Exploiting TVWS Spectrums Based on Modified PSO,” IEEE Open Journal of Vehicular Technology, vol. 3, pp. 356–366, 2022, doi: 10.1109/OJVT.2022.3191762.

M. Abdel-Basset, R. Mohamed, M. Elhoseny, A. K. Bashir, A. Jolfaei, and N. Kumar, “Energy-Aware Marine Predators Algorithm for Task Scheduling in IoT-Based Fog Computing Applications,” IEEE Trans Industr Inform, vol. 17, no. 7, pp. 5068–5076, Jul. 2021, doi: 10.1109/TII.2020.3001067.

M. Liu et al., “A Slime Mold-Ant Colony Fusion Algorithm for Solving Traveling Salesman Problem,” IEEE Access, vol. 8, pp. 202508–202521, 2020, doi: 10.1109/ACCESS.2020.3035584.

D. Dhawale, V. K. Kamboj, and P. Anand, “An effective solution to numerical and multi-disciplinary design optimization problems using chaotic slime mold algorithm,” Eng Comput, May 2021, doi: 10.1007/s00366-021-01409-4.

O. Altay, “Chaotic slime mould optimization algorithm for global optimization,” Artif Intell Rev, vol. 55, no. 5, pp. 3979–4040, Jun. 2022, doi: 10.1007/s10462-021-10100-5.

M. A. A. Al-qaness, A. A. Ewees, H. Fan, L. Abualigah, and M. Abd Elaziz, “Marine Predators Algorithm for Forecasting Confirmed Cases of COVID-19 in Italy, USA, Iran and Korea,” Int J Environ Res Public Health, vol. 17, no. 10, p. 3520, May 2020, doi: 10.3390/ijerph17103520.

I. Rejer and J. Jankowski, “fGAAM: A fast and resizable genetic algorithm with aggressive mutation for feature selection,” Pattern Analysis and Applications, vol. 25, no. 2, pp. 253–269, May 2022, doi: 10.1007/s10044-021-01000-z.

S. J. Sajadi and A. Ahmadi, “An integrated optimization model and metaheuristics for assortment planning, shelf space allocation, and inventory management of perishable products: A real application,” PLoS One, vol. 17, no. 3, p. e0264186, Mar. 2022, doi: 10.1371/journal.pone.0264186.

T. Krityakierne, O. Limphattharachai, and W. Laesanklang, “Nurse-patient relationship for multi-period home health care routing and scheduling problem,” PLoS One, vol. 17, no. 5, p. e0268517, May 2022, doi: 10.1371/journal.pone.0268517.



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