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.

合作伙伴选择问题（PSP）是构成和重新配置战略联盟的关键问题。在本文中，我们力求在时间，预算，活动优先级和资源限制下解决PSP。考虑到多个目标，我们建议的方法同时最小化总成本和项目工期，同时最大化平均质量。为了这些目的，我们提出了一种新颖的多目标共生生物搜索计划表（MOSOSS）。在这种新算法中，对演化算子进行了完全重新设计，以进行组合优化。此外，它们特别适用于计划问题。新的MOSOSS算法的一个值得注意的原始方面是，它改进了部分（未完全调度）的解决方案。以此目的，我们建议进化运算符是专门为处理不完整和完整的计划而构造的。在随机生成的PSP实例上的实验结果表明，与现有的多目标共生生物搜索和NSGA-II相比，MOSOSS可以更好地覆盖Pareto前沿。