Work

Multiple Lagrange Multiplier Method for Constrained Evolutionary Optimization

Year: 2000

Type: article

Abstract: One of the well-known problems in evolutionary search for solving optimization problem is the premature convergence. The general constrained optimization techniques such as hybrid evolutionary programming, two-phase evolutionary programming, and Evolian algorithms are not safe from the same problem in the first phase. To overcome this problem, we apply the sharing function to the Evolian algorithm and propose to use the multiple Lagrange multiplier method for the subsequent phases of Evolian. The method develops Lagrange multipliers in each subpopulation region independently and seeks for multiple global optima, if any, in parallel. The simulation results demonstrate the usefulness of the proposed multiple Lagrange multiplier method.

Source: Journal of Advanced Computational Intelligence and Intelligent Informatics

DOI: 10.20965/jaciii.2000.p0158

Authors: Hyun Myung, Jong-Hwan Kim

Language: English

Topic: Metaheuristic Optimization Algorithms Research

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

OpenAlex ID: W318488473

FWCI:

Cites:

Cited by: 2

Related to: 10