Abstract
This paper presents a new archiving strategy and some modified search heuristics for the Multi Agent Collaborative Search algorithm (MACS). MACS is a memetic scheme for multi-objective optimisation that combines the local exploration of the neighbourhood of some virtual agents with social actions to advance towards the Pareto front. The new archiving strategy is based on the physical concept of minimising the potential energy of a cloud of points each of which repels the others. Social actions have been modified to better exploit the information in the archive and local actions dynamically adapt the maximum number of coordinates explored in the pattern search heuristic. The impact of these modifications is tested on a standard benchmark and the results are compared against MOEA/D and a previous version of MACS. Finally, a real space related problem is tackled.
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References
Zuiani, F., Vasile, M.: Preliminary design of debris removal missions by means of simplified models for low-thrust, many-revolution transfers. Int. J. Aerosp. Eng. (2012)
Zuiani, F., Kawakatsu, Y., Vasile, M.: Multi-objective optimisation of many-revolution, low-thrust orbit raising for destiny mission. In: 23rd AAS/AIAA Space Flight Mechanics Conference (2013)
Vasile, M., Zuiani, F.: Multi-agent collaborative search: an agent-based memetic multi-objective optimization algorithm applied to space trajectory design. Proc. Inst. Mech. Eng. Part G: J. Aerosp. Eng. 225(11), 1211–1227 (2011)
Zuiani, F., Vasile, M.: Improved individualistic actions for multi-agent collaborative search. In: EVOLVE (2013)
Zuiani, F., Vasile, M.: Multi agent collaborative search based on tchebycheff decomposition. Comput. Optim. Appl. 56(1), 189–208 (2013)
Zuiani, F, Vasile, M.: Multi agent collaborative search with thecycheff decomposition and monotonic basin hopping. In: BIOMA, May 2012
Zhang, Q., Zhou, A., Zhao, S., Suganthan, P.N., Liu, W., Tiwari, S.: Multiobjective optimization test instances for the cec 2009 special session and competition. University of Essex, Colchester, UK and Nanyang technological University, Singapore, special session on performance assessment of multi-objective optimization algorithms, technical report, pp. 1–30 (2008)
Zhang, Q., Liu, W., Li, H.: The performance of a new version of moea/d on cec09 unconstrained mop test instances. IEEE Congr. Evolut. Comput. 1, 203–208 (2009)
Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.A.M.T.: A fast and elitist multiobjective genetic algorithm: Nsga-ii. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)
Zitzler, E., Deb, K., Thiele, L.: Comparison of multiobjective evolutionary algorithms: empirical results. Evol. Comput. 8(2), 173–195 (2000)
Schütze, O., Esquivel, X., Lara, A., Coello, C.A.C.: Using the averaged hausdorff distance as a performance measure in evolutionary multiobjective optimization. IEEE Trans. Evol. Comput. 16(4), 504–522 (2012)
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Ricciardi, L.A., Vasile, M. (2019). Improved Archiving and Search Strategies for Multi Agent Collaborative Search. In: Minisci, E., Vasile, M., Periaux, J., Gauger, N., Giannakoglou, K., Quagliarella, D. (eds) Advances in Evolutionary and Deterministic Methods for Design, Optimization and Control in Engineering and Sciences. Computational Methods in Applied Sciences, vol 48. Springer, Cham. https://doi.org/10.1007/978-3-319-89988-6_26
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DOI: https://doi.org/10.1007/978-3-319-89988-6_26
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