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Several bioinspired algorithms, that is, based on the behavior of living beings of nature, have been proposed in the literature with the objective of solving optimization problems.
The objective of solving this problem is to minimize the costs of delivering all commodities by deciding which inter-terminal blocks to build and by specifying the assignment of commodities to these blocks, while observing limits on the number and cumulative volume of the blocks assembled at each terminal.
There were reports in the interviews of proposed changes in the center after the network was implemented, with the objective of solving problems based on identified shortcomings.
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Due to the difficulty of solving multi-objective problems using multi-objective algorithms and the lack of enough expertise, researchers in different fields tend to aggregative objectives and use single-objective algorithms.
In this paper new evolutionary method of solving multi-objective optimization problems is presented.
Many methods are capable of solving multi-objective optimization problems (MOOPs) to obtain the Pareto front [ 30- 32] and generally fall into one of two categories: generating methods and preference-based methods.
The co-existence of discrete and continuous independent variables in an engineering optimization problem with a multimodal objective function makes many methods incapable of solving the problem.
The purpose of solving a single-objective problem is to improve the unique performance index that should be the minimum or maximum.
In this study, the "model pre-emption" strategy is introduced for increasing the efficiency of solving such multi-objective optimization problems.
The closed loop control formulation of DPS improves the quality and robustness of key management tradeoffs, while dramatically reducing the computational complexity of solving the multi-objective pollution control problem relative to open loop control strategies.
With the help of the EDA based reproduction, the algorithm can lean linkage between decision variables and hence have a good capability of solving complex multi-objective optimization problems, such as the MO-RFCO problem.
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