NDLI: Incremental Refinement of Solutions for Dynamic Multi Objective Optimization Problems

Content Provider	IEEE Xplore Digital Library
Author	Mariano-Romero, C.E. Morales, M.E.F.
Copyright Year	2007
Description	Author affiliation: Mexican Inst. of Water Technol., Jiutepec (Mariano-Romero, C.E.) \|\| INAOE, Puebla (Morales, M.E.F.)
Abstract	MDQL is an algorithm, based on reinforcement learning, for solving multiple objective optimization problems, that has been tested on several applications with promising results. MDQL discretizes the decision variables into a set of states, each associated with actions to move agents to contiguous states. A group of agents explore this state space and are able to find Pareto sets applying a distributed reinforcement learning algorithm. The precision of the Pareto solutions depends on the chosen granularity of the states. A finer granularity on the states creates more precise solutions but at the expense of a larger search space, and consequently the need for more computational resources. In this paper, a very important improvement is introduced into the original MDQL algorithm to incrementally refined the Pareto solutions. The new algorithm, called IMDQL, starts with a coarse granularity to find an initial Pareto set. A vicinity for each of the Pareto solutions in refined and a new Pareto set is founded in this refined state space. This process continues until there is no more improvement within a small threshold value. It is shown that IMDQL not only improves the solutions found by MDQL, but also converges faster. MDQL has also been tested on the solutions of dynamic optimization problems. In this paper, it is also shown that the adaptation capabilities observed in MDQL can be improved with IMDQL. IMDQL was tested on the benchmark problems proposed by Jin. Performance evaluation was made using the Collective Mean Fitness metric proposed by Morrison. IMDQL was compared against an standard evolution strategy with the covariance matrix adaptation (CMA-ES) with very promising results.
Starting Page	255
Ending Page	263
File Size	176471
Page Count	9
File Format	PDF
ISBN	9780769531243
DOI	10.1109/MICAI.2007.47
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-11-04
Publisher Place	Mexico
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Learning Constraint optimization Dynamic Multi Objective Optimization Incremental Refinement Evolutionary computation Benchmark testing Pareto optimization Entropy Space exploration State-space methods Covariance matrix Artificial intelligence
Content Type	Text
Resource Type	Article

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