Multi-agent collaborative search with Tchebycheff decomposition and monotonic basin hopping steps

Federico Zuiani, Massimiliano Vasile

Research output: Contribution to conferencePaper

4 Citations (Scopus)

Abstract

This paper presents a novel formulation of Multi Agent Collaborative
Search for multiobjective optimization. A population of agents combines
global exploration heuristics and moves to explore the neighborhood of
each agent. In this novel formulation the selection process is based on
the Tchebycheff decomposition of the multiobjective optimization problem
into single objective optimization problems in combination with the
use of the dominance index. The decomposition allows the implementation
of Monotonic Basin Hopping steps that improve convergence on
single funnel structures. The novel agent-based algorithm is tested on
a standard benchmark and on a real space trajectory design problem.
Its performance is compared against a number of state-of-the-art multiobjective optimization algorithms.
Original languageEnglish
Publication statusPublished - 24 May 2012
EventBioinspired Optimization Methods and their Applications, BIOMA 2012 - Bohinj, Slovenia
Duration: 24 May 201225 May 2012

Conference

ConferenceBioinspired Optimization Methods and their Applications, BIOMA 2012
CountrySlovenia
CityBohinj
Period24/05/1225/05/12

Keywords

  • multi-objective optimisation
  • multi-agent paradigm
  • memetic algorithms

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  • Cite this

    Zuiani, F., & Vasile, M. (2012). Multi-agent collaborative search with Tchebycheff decomposition and monotonic basin hopping steps. Paper presented at Bioinspired Optimization Methods and their Applications, BIOMA 2012, Bohinj, Slovenia.