Abstract
During the last decade novel computational methods have been introduced in some fields of engineering sciences. In this article, we describe a novel Particle Swarm Optimization (PSO) approach to multi-objective optimization, called Time Variant Multi-Objective Particle Swarm Optimization (TV-MOPSO). The mechanical and tribological behaviors of sintered steel have been experimentally investigated. TV-MOPSO is made adaptive in nature by allowing its vital parameters to change with iterations. This adaptiveness helps the algorithm to explore the search space more efficiently. A new diversity parameter has been used to ensure sufficient diversity amongst the solutions of the non-dominated fronts, while retaining at the same time the convergence to the Pareto-optimal front.

Key words
wear, steel, swarm

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