For multiple objectives continuous space optimization problem, Zhan et al. [16] proposed a co-evolutionary technique for multiple objectives optimization problem.
A multiple objective optimization model is proposed to optimize inspection security, inspection cost, and processing time; an evolutionary approach is used to solve the model.
Since most real-life problems include conflicting objectives, multiple objective optimization provides a means for obtaining more realistic models.
The multiple objective optimization problems are solved by an evolutionary algorithm using a Matlab program.
To balance these conflicts, we treated each training set as a separate objective in a multiple objective optimization calculation.
This clustering method considers two types of criteria as multiple objectives and optimizes them simultaneously by using a modified multi-objective evolutionary algorithm with new encoding and operators.
There are two general approaches to multiple-objective optimization.
We use the Nash-PSO algorithm to build Nash equilibrium for multiple-objective optimization.
Multi-objective optimization extends optimization theory by permitting multiple objectives to be optimized simultaneously.
In the same spirit of simplicity, multiple objectives such as optimizing financial returns subject to ecological constraints, of vice-versa, have been treated here with linear models.
