Authors:
Aisha Umair
1
;
Anders Clausen
1
;
Yves Demazeau
2
and
Bo Nørregaard Jørgensen
1
Affiliations:
1
Center for Energy Informatics, University of Southern Denmark, Campusvej 55, Odense and Denmark
;
2
Laboratoire d’Informatique de Grenoble, Batiment IMAG, 700 avenue Centrale, Grenoble and France
Keyword(s):
Evolutionary Computation, Multi-objective Optimization, Social Welfare, Energy Systems.
Related
Ontology
Subjects/Areas/Topics:
Energy and Economy
;
Energy-Aware Systems and Technologies
;
Evolutionary Algorithms in Energy Applications
;
Optimization Techniques for Efficient Energy Consumption
Abstract:
Multi-agent resource allocation refers to the distribution of resources among agents. Resource allocation can be particularly challenging if the agents have conflicting objectives over multiple interdependent issues. In such cases, multi-objective optimization methods can be used to find an optimal allocation of resources, that maximizes social welfare. Social welfare refers to the welfare of the entire society of agents and therefore considered as a suitable metric for assessing the overall system performance in multi-agent resource allocation. In this paper we study and discuss different notions of social welfare and investigate their impact on the optimization outcome specifically for the problems comprising multiple conflicting objectives with interdependent issues. To this end, we implement and apply different notions of social welfare to a real-world, complex problem, where a resource domain is responsible for making allocation of energy resources to multiple energy intensive c
onsumers (Commercial Greenhouse Growers). The problem is modeled as a multi-objective optimization context. Our results show how different social welfare methods affect the optimization outcome and result in different socially optimal resource allocations, depending on the behavior we expect from the system.
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