SOLVING CONSTRAINED OPTIMIZATION PROBLEMS USING PROBABILITY COLLECTIVES AND A PENALTY FUNCTION APPROACH

DOI10.1142/S1469026811003185zbMath1251.68207MaRDI QIDQ4650013

K. Tai, Anand J. Kulkarni

Publication date: 15 November 2012

Published in: International Journal of Computational Intelligence and Applications (Search for Journal in Brave)

zbMATH Keywords

Nash equilibriummulti-agent systemsociophysicscollective intelligenceprobability collectives

Mathematics Subject Classification ID

Problem solving in the context of artificial intelligence (heuristics, search strategies, etc.) (68T20)

Related Items (2)

An Improved Cohort Intelligence with Panoptic Learning Behavior for Solving Constrained Problems ⋮ A Probability Collectives Approach for Multi-Agent Distributed and Cooperative Optimization with Tolerance for Agent Failure

Cites Work

• Unnamed Item
• An application of swarm optimization to nonlinear programming
• An efficient constraint handling method for genetic algorithms
• Multiagent learning using a variable learning rate
• Cultured differential evolution for constrained optimization
• Adopt: asynchronous distributed constraint optimization with quality guarantees
• Derivative-free filter simulated annealing method for constrained continuous global optimization
• Constraint handling in genetic algorithms using a gradient-based repair method
• Min-Cost Selfish Multicast With Network Coding
• Optimization of trusses using genetic algorithms for discrete and continuous variables
• ON THE USE OF INTELLIGENT AGENTS TO GUIDE TEST SEQUENCE SELECTION AND OPTIMIZATION

This page was built for publication: SOLVING CONSTRAINED OPTIMIZATION PROBLEMS USING PROBABILITY COLLECTIVES AND A PENALTY FUNCTION APPROACH