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Multi-objective Evolutionary Algorithms (MOEAs) always approximate a set of optimal solutions. This set is required to be well spread and uniformly covering wide area of the Pareto-optimal front. In practical context, the Decision Maker (DM) usually chooses solutions in the middle of objective space, where the surface bulges out the most. Such solutions are called “knee solutions”. They are the most...
Helping the human decision maker (DM) in finding the most preferred solution is the main purpose of Preference-based Multi-Objective Evolutionary Algorithm (P-MOEA). Interactive methods have been increasingly developed during the years because the preference information is less explicit than other methods. In addition, the DM can specify and correct his/her preference during the course of optimization...
The Generation Expansion Planning (GEP) problem applies to the expansion of the electricity generation network with new power plant investments. It is a multi-objective optimization problem where the level of uncertainty is very high because of its spatial dimensions and time scale. Therefore, it becomes necessary to introduce in the decision making process a systematic treatment of uncertainty to...
Nowadays, usage of optimization methods and techniques in various applications is the key factor of increasing the systems efficiency and performance. In this paper, Proportional-Integral-Derivative (PID) controller optimization in greenhouse lighting control system is studied by tuning PID controller coefficients. The advantages and disadvantages of employing multi-objective optimization methods...
Because of non-existence of an ideal single solution in Multi-objective optimization frameworks, the set of optimal solutions is required to be well spread and uniformly covering wide area of Pareto front. The decision maker (DM) still work hard to compromise the trade-offs solutions based on his/her preferences. In this paper, we proposed a pruning algorithm that can filter out undesired solutions...
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