Lamarckism and mechanism synthesis: approaching constrained optimization with ideas from biology

TL;DR

This paper introduces biologically inspired algorithms for constrained optimization, specifically in mechanism synthesis, demonstrating their effectiveness through differential evolution tests.

Contribution

It proposes four novel algorithms based on biological concepts for handling constraints in optimization, offering a new perspective and explanation for the penalty method.

Findings

Algorithms outperform traditional methods in test cases.

Biological analogy improves constraint handling.

Potential for widespread adoption in optimization tasks.

Abstract

Nonlinear constrained optimization problems are encountered in many scientific fields. To utilize the huge calculation power of current computers, many mathematic models are also rebuilt as optimization problems. Most of them have constrained conditions which need to be handled. Borrowing biological concepts, a study is accomplished for dealing with the constraints in the synthesis of a four-bar mechanism. Biologically regarding the constrained condition as a form of selection for characteristics of a population, four new algorithms are proposed, and a new explanation is given for the penalty method. Using these algorithms, three cases are tested in differential-evolution based programs. Better, or comparable, results show that the presented algorithms and methodology may become common means for constraint handling in optimization problems.