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Science is good at solving constrained problems, such as "what is the structure of DNA?" or "how do you land on the moon?" Or even "what are the Holocene boundaries?".
The gradient-projection algorithm (GPA) plays an important role in solving constrained convex minimization problems.
The well-known approach to solving constrained optimization problems is the method of Lagrange multipliers.
Moreover, it is capable of solving constrained fractional programming problems as a special case.
For solving constrained problems, the classic Penalty and Barrier functions were included in the API.
For solving constrained convex minimization problems, some methods were proposed by some authors (see [4] and [5]).
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A hybrid genetic algorithm-based method to solve constrained multi-objective optimization problems is proposed.
This study focuses on the modification of Tree-Seed Algorithm (TSA) to solve constrained optimization problem.
A new evolutionary algorithm, Backtracking Search Algorithm (BSA), is applied to solve constrained optimization problems.
A modified Artificial Bee Colony algorithm to solve constrained numerical optimization problems is presented in this paper.
This paper provides a survey of the most important repair heuristics used in evolutionary algorithms to solve constrained optimization problems.
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CEO of Professional Science Editing for Scientists @ prosciediting.com