Meta-Lamarckian learning in multi-objective optimization for mobile social network search

Abstract

Mobile Social Networks (MSNs) have recently brought a revolution in socially-oriented applications and services for mobile phones. In this paper, we consider the search problem in a MSN that aims at simultaneously maximizing the user's search outcome (recall) and mobile phone performance (battery usage). Because of the conflicting nature of these two objectives, the problem is dealt within the context of Multi-Objective Optimization (MOO). Our proposed approach hybridizes a Multi-objective Evolutionary Algorithm based on Decomposition (MOEA/D) with a Meta-Lamarckian (ML) learning strategy that learns from the problem's properties and objective functions. The ML strategy is devised for adaptively select the best performing local search heuristic for each case, from a pool of general-purpose heuristics, so as to locally optimize the solutions during the evolution. We evaluated our propositions on a realistic multi-objective MSN search problem using trace-driven experiments with real mobility and social patterns. Extensive experimental studies reveal that the proposed method successfully learns the behaviour of individual local search heuristics during the evolution, adaptively follows the pattern of the best performing heuristics at different areas of the objective space and offers better performance in terms of both convergence and diversity than its competitors.The proposed Meta-Lamarckian based MOEA does not utilize any problem-specific heuristics, as most cases in the literature do, facilitating its applicability to other combinatorial MOO problems. To test its generalizability the proposed method is also evaluated on various test instances of the well-studied multi-objective Permutation Flow Shop Scheduling Problem.