Semi-Steady-State Jaya Algorithm for Optimization
Source abstract
Abstract
The Jaya algorithm is arguably one of the fastest-emerging metaheuristics amongst the newest members of the evolutionary computation family. The present paper proposes a new, improved Jaya algorithm by modifying the update strategies of the best and the worst members in the population. Simulation results on a twelve-function benchmark test-suite and a real-world problem show that the proposed strategy produces results that are better and faster in the majority of cases. Statistical tests of significance are used to validate the performance improvement.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Metaheuristic Optimization Algorithms Research · Evolutionary Algorithms and Applications · Advanced Multi-Objective Optimization Algorithms
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Computer Science, University of Missouri, St. Louis, MO 63121, USA
Funding
NSF
References (31)
Show source-provided references
1. Michalewicz, Z. (2013). Genetic Algorithms+ Data Structures = Evolution Programs, Springer Science & Business Media.
2. Goodfellow, I., Bengio, Y., and Courville, A. (2016). Deep Learning, MIT Press.
3. No free lunch theorems for optimization10.1109/4235.585893 ↗
4. Jaya: A simple and new optimization algorithm for solving constrained and unconstrained optimization problems
5. Multi-team perturbation guiding Jaya algorithm for optimization of wind farm layout10.1016/j.asoc.2018.07.036 ↗
6. Application of EOS-ELM with binary Jaya-based feature selection to real-time transient stability assessment using PMU data10.1109/ACCESS.2017.2765626 ↗
7. Pampara, G., Franken, N., and Engelbrecht, A.P. (2005, January 2–5). Combining particle swarm optimisation with angle modulation to solve binary problems. Proceedings of the 2005 IEEE Congress on Evolutionary Computation, Scotland, UK.
8. A self-adaptive multi-population based Jaya algorithm for engineering optimization10.1016/j.swevo.2017.04.008 ↗
9. Multi-objective optimization of abrasive waterjet machining process using Jaya algorithm and PROMETHEE Method10.1007/s10845-017-1373-8 ↗
10. An efficient multi-core implementation of the Jaya optimisation algorithm10.1080/17445760.2017.1416387 ↗
11. Development of pathological brain detection system using Jaya optimized improved extreme learning machine and orthogonal ripplet-II transform10.1007/s11042-017-5281-x ↗
12. Improved teaching–learning-based and JAYA optimization algorithms for solving flexible flow shop scheduling problems10.1007/s40092-017-0244-4 ↗
13. A prediction model-guided Jaya algorithm for the PV system maximum power point tracking10.1109/TSTE.2017.2714705 ↗
14. Jaya based ANFIS for monitoring of two class motor imagery task10.1109/ACCESS.2016.2637401 ↗
15. Gao, K., Zhang, Y., Sadollah, A., and Su, R. (2016, January 13–15). Jaya algorithm for solving urban traffic signal control problem. Proceedings of the 2016 14th International Conference on Control, Automation, Robotics and Vision (ICARCV), Phuket, Thailand.10.1109/ICARCV.2016.7838661 ↗
16. A study of reproduction in generational and steady-state genetic algorithms
17. Analysis of selection algorithms: A Markov chain approach10.1162/evco.1996.4.2.133 ↗
18. PEM fuel cell modeling using differential evolution10.1016/j.energy.2012.01.039 ↗
19. A literature survey of benchmark functions for global optimisation problems
20. Johnson, R.A., Miller, I., and Freund, J.E. (2000). Probability and Statistics for Engineers, Pearson Education.
21. Larminie, J., Dicks, A., and McDonald, M.S. (2003). Fuel Cell Systems Explained, J. Wiley.10.1002/9781118878330 ↗
22. O’hayre, R., Cha, S.W., Colella, W., and Prinz, F.B. (2016). Fuel Cell Fundamentals, John Wiley & Sons.10.1002/9781119191766 ↗
23. Proton exchange membrane (PEM) fuel cell stack configuration using genetic algorithms10.1016/j.jpowsour.2004.01.012 ↗
24. Chakraborty, U.K. (2019). Proton exchange membrane fuel cell stack design optimization using an improved Jaya algorithm. Energies, 12.10.3390/en12163176 ↗
25. Using qualimetric engineering and extremal analysis to optimize a proton exchange membrane fuel cell stack10.1016/j.apenergy.2014.04.058 ↗
26. Chakraborty, U.K. (2019). A new model for constant fuel utilization and constant fuel flow in fuel cells. Appl. Sci., 9.10.3390/app9061066 ↗
27. Behavior of crossover operators in NSGA-III for large-scale optimization problems10.1016/j.ins.2018.10.005 ↗
28. Michalewicz, Z., and Fogel, D.B. (2013). How to Solve It: Modern Heuristics, Springer Science & Business Media.
29. Chakraborty, U.K. (2008). Advances in Differential Evolution, Springer.10.1007/978-3-540-68830-3 ↗
30. Particle swarm optimization for single objective continuous space problems: A review10.1162/EVCO_r_00180 ↗
31. A comparative study of artificial bee colony algorithm