MOSA method: a tool for solving multiobjective combinatorial optimization problems

Abstract

The success of modern heuristics (Simulated Annealing (S.A.), Tabu Search, Genetic Algorithms, . . . ) in solving classical combinatorial optimization problems has drawn the attention of the research community in multicriteria methods.In fact, for large-scale problems, the simultaneous difficulties of NP-hard complexity and of multiobjective framework make most Multiobjective Combinatorial Optimization (MOCO) problems intractable for exact methods.This paper develops the so-called MOSA (Multiobjective Simulated Annealing) method to approximate the set of efficient solutions of a MOCO problem. Different options for the implementation are illustrated and extensive experiments prove the efficiency of the approach. Its results are compared to exact methods on bi-objective knapsack problems.

On Pareto-Koopmans Efficiency for Performance-Driven Optimisation in Self-Organising Networks

“…In fact, the methodology is based on a concept of Tabu search into SA, which creates the archiving process as a memory for the optimisation process. A self-similar extension to SA was offered in [23] for MOSA. In this approach, improvement or deterioration with respect to the objective functions are accepted based on probabilities for each move.…”

Performance assessment of multiobjective optimizers: an analysis and review

“…However, if we consider just the total number (rather than the ratio) of Paretooptimal points in the approximation set, we obtain -compatibility. This also holds for the indicator used in [20], which gives the ratio of the number of Pareto-optimal solutions in A to the cardinality of the Pareto-optimal front. Nevertheless, the power of these comparison methods is limited because none of them is complete with respect to any dominance relation.…”

Peer Review #1 of "Multi-objective simulated annealing for hyper-parameter optimization in convolutional neural networks (v0.1)"

“…There are several approaches to combine all objective function values into a single value. (Ulungu et al, 1999) use a criterion scalarizing function which takes the weighted sum of the objective functions. (Czyzzak, Jaszkiewicz, 1998) use a diversified set of weights to obtain a diverse set of solutions in the final front in their SA-based algorithm which they call Pareto Simulated Annealing (PSA).…”