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A Hybrid Distribution Algorithm Based on Membrane Computing for Solving the Multiobjective Multiple Traveling Salesman Problem

He J., Zhang K.

Fundamenta Informaticae

2015

Vol. 136, nr 3

199--208

The multiobjective multiple traveling salesman problem (MmTSP), in which multiple salesmen and objectives are involved in a route, is known to be NP-hard. The MmTSP is more appropriate for real-life applications than the classical traveling salesman problem (TSP), however it has not received the same amount of attention. Due to the high complexity of the MmTSP, a promising algorithm for solving it must be based on a global search procedure. This paper proposes a hybrid global search algorithm, that belongs to the membrane computing framework. The search behavior of the algorithm is enhanced by a communication mechanism. The multichromosome representation is considered to reduce the excess runtime. The effectiveness of the proposed algorithm is tested on the MmTSP with disparately-scaled objective functions, salesmen and problem sizes. The experimental results are compared with the NSGA-II and several evolutionary strategies with decomposition, including simulated annealing algorithm, hill climbing method, pure evolutionary algorithm, and genetic algorithm.

Interactive evolutionary multiobjective optimization driven by robust ordinal regression

Branke J., Greco S., Słowiński R., Zielniewicz P.

Bulletin of the Polish Academy of Sciences. Technical Sciences

2010

Vol. 58, nr 3

347-358

This paper presents the Necessary-preference-enhanced Evolutionary Multiobjective Optimizer (NEMO), which combines an evolutionary multiobjective optimization with robust ordinal regression within an interactive procedure. In the course of NEMO, the decision maker is asked to express preferences by simply comparing some pairs of solutions in the current population. The whole set of additive value functions compatible with this preference information is used within a properly modified version of the evolutionary multiobjective optimization technique NSGA-II in order to focus the search towards solutions satisfying the preferences of the decision maker. This allows to speed up convergence to the most preferred region of the Pareto-front.