The single-objective version of stochastic paint optimizer (SPO) is appropriately changed to solve multi-objective optimization problems described as MOSPO. Color theory, the color wheel, and color combination methods are the main concepts of SPO. The SPO will be able to do excellent exploration and exploitation thanks to four simple color combination rules that do not have any internal parameters. Principles like using of fixed-sized external archive make the recommended technique various from the initial single-objective SPO. In addition, to perform multi-objective optimization, the leader selection feature has been added to SPO. The efficiency of recommended multi-objective stochastic paint optimizer (MOSPO) is tested on ten mathematical (CEC-09) and eight multi-objective engineering design problems concerning remarkable precision and uniformity compared to multi-objective particle swarm optimization (MOPSO), multi-objective slap swarm algorithm (MSSA), and multi-objective ant lion optimizer. According to the results of different performance metrics, such as generational distance (GD), inverted generational distance (IGD), maximum spread, and spacing, the proposed algorithm can provide quality Pareto fronts with very competitive results with high convergence.
- Evolutionary computation
- Multi-objective engineering problems
- Multi-objective optimization problems
- Multi-objective stochastic paint optimizer
- Stochastic paint optimizer