Authors:
Joerg Bremer
1
and
Sebastian Lehnhoff
2
Affiliations:
1
Department of Computing Science, University of Oldenburg, Uhlhornsweg, Oldenburg and Germany
;
2
R&D Division Energy, OFFIS – Institute for Information Technology, Escherweg, Oldenburg and Germany
Keyword(s):
Global Optimization, Distributed Optimization, Multi-agent Systems, Lazy Agents, Coordinate Descent Optimization.
Related
Ontology
Subjects/Areas/Topics:
Agents
;
Artificial Intelligence
;
Artificial Intelligence and Decision Support Systems
;
Computational Intelligence
;
Distributed Problem Solving
;
Enterprise Information Systems
;
Evolutionary Computing
;
Informatics in Control, Automation and Robotics
;
Intelligent Control Systems and Optimization
;
Self Organizing Systems
;
Soft Computing
;
Swarm Intelligence
Abstract:
Optimization problems with rugged, multi-modal Fitness landscapes, non-linear problems, and derivative-free optimization entails challenges to heuristics especially in the high-dimensional case. High-dimensionality also tightens the problem of premature convergence and leads to an exponential increase in search space size. Parallelization for acceleration often involves domain specific knowledge for data domain partition or functional or algorithmic decomposition. We extend a fully decentralized agent-based approach for a global optimization algorithm based on coordinate descent and gossiping that has no specific decomposition needs and can thus be applied to arbitrary optimization problems. Originally, the agent method suffers from likely getting stuck in high-dimensional problems. We extend a laziness mechanism that lets the agents randomly postpone actions of local optimization and achieve a better avoidance of stagnation in local optima. The extension is tested against the origin
al method as well as against established methods. The lazy agent approach turns out to be competitive and often superior in many cases.
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