MOSOSS: an adapted multi-objective symbiotic organisms search for scheduling

Abstract

The partner selection problem (PSP) is a key issue in constituting and reconfiguring strategic alliances. In this paper, we seek to address PSP under time, budget, activity precedence, and resource constraints. Multiple objectives are considered, our proposed approach simultaneously minimizing total cost and project duration while maximizing average quality. For these purposes, we present a novel multi-objective symbiotic organisms search for scheduling (MOSOSS). In this new algorithm, evolutionary operators are completely redesigned for combinatorial optimization. Furthermore, they are specifically adapted for scheduling problems. One notable original aspect of the new MOSOSS algorithm is that it evolves partial (incompletely scheduled) solutions. For this purpose, we propose evolutionary operators specially constructed to deal with both incomplete and complete schedules. Experimental results on randomly generated PSP instances show that MOSOSS offers a better coverage of the Pareto front as compared to the extant multiple objective symbiotic organisms search and NSGA-II.