Abstract: Solving constrained multi-objective optimization problems (CMOPs) is a challenging task due to the presence of multiple conflicting objectives and intricate constraints. In order to better ...
Incorporating multiple constraints such as task completion time, UAV payload capacity, and flight range into path optimization algorithms allows for more efficient search patterns.
Abstract: Multi-objective evolutionary algorithms (MOEAs) have demonstrated significant success in solving multi-objective optimization problems (MOPs). However, their performance is highly sensitive ...
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