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In addition to the unconstrained optimization problem, there is also work involved in the multiple objectives optimization problem under constrained condition [19].
For multiple objectives continuous space optimization problem, Zhan et al. [16] proposed a co-evolutionary technique for multiple objectives optimization problem.
In this paper, an effective multiple objectives optimization method is discussed assuming that a distribution power system where many RES DGs are installed.
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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.
The set Ξ of non-dominated (or Pareto-optimal) designs for the multiple objective optimization problem defined by log| M β |, and log| M ν | is defined by The solid line in Figure 8 is an example of a Pareto surface for simultaneous maximization of the two criteria.
The approach builds upon multiple objective optimization in order to generate a set of optimal solvent molecules that embeds highly inclusive designs and incorporates comprehensive design information regardless of process economics.
Since most real-life problems include conflicting objectives, multiple objective optimization provides a means for obtaining more realistic models.
Interested readers may find the importance of generalized convexity to variational inequalities and multiple objective optimization in [16 20].
The multiple objective optimization problems are solved by an evolutionary algorithm using a Matlab program.
Furthermore, minimizing CP and amino acid nutrients requires a multiple objective optimization, which is not the objective of the current study.
This work proposes new multiple objective optimization (MOO) technology, using a Monte Carlo-based algorithm stemmed from simulated annealing (SA).
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