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\begin{document}

\title{Multi Agent Collaborative Search Algorithm with Adaptive Weights}



\author[1]{Li Cao}%
\author[1]{Maocai Wang}%
\author[1]{Guangming Dai}%
\author[2]{Max Vassile}%
\affil[1]{China University of Geosciences College of Computer Science}%
\affil[2]{University of Strathclyde Department of Mechanical and Aerospace Engineering}%


\vspace{-1em}



  
  \date{June 17, 2024}


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\begin{abstract}
This paper presents a new Multi Agent Collaborative Search Algorithm
with Adaptive Weights (named MACSAW). MACS is a memetic scheme for
multi-objective optimization which contains two kind of actions, the
local actions and social actions. The former explore the neighborhood of
some virtual agents and the latter push the individual towards the
Pareto front. On the base of the latest version of MACS, MACS2.1, we
improve the old algorithm from three direction. First, a new kind of
utility function is introduced to enhance the convergence. Next, a new
social action process which contains more operators and adaptive
parameters is embedded in MACSAW. Finally, MACS2.1 lacks the weight
vectors adjustment process which leads to diversity losing in some real
problems and MACSAW adds it. Further, MACSAW is compared with some
state-of-art algorithms and MACS2.1 on some standard benchmarks. It gets
competitive results. Two real optimization problems is tackled and the
results are analyzed in details.%
\end{abstract}%



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