论文标题
一种基于社会物理学的混合元疗法，用于解决复杂的非凸的约束优化问题
A socio-physics based hybrid metaheuristic for solving complex non-convex constrained optimization problems
论文作者
论文摘要
到目前为止，已经开发了几种基于人工智能的启发式和元启发式算法。这些算法已经表明了它们在解决不同领域的复杂问题方面的优势。但是，有必要批判性地验证这些算法以解决现实世界中约束优化问题。这些问题中的搜索行为不同，因为它涉及大量线性，非线性和非凸型平等和不平等约束。在这项工作中，使用两种源自基于社会的队列智能（CI）算法的两种约束元启发式算法来解决57个现实世界中约束优化问题测试套件。第一个基于CI的算法结合了自适应惩罚函数方法，即CI-SAPF。第二算法将CI-SAPF与所引用的CI-SAPF-CBO的基于物理碰撞物体优化（CBO）的内在特性相结合。将从CI-SAPF和CI-SAPF-CBO获得的结果与其他约束优化算法进行了比较。提出的算法的优越性将在详细信息中讨论，然后将来的方向发展受约束的处理技术。
Several Artificial Intelligence based heuristic and metaheuristic algorithms have been developed so far. These algorithms have shown their superiority towards solving complex problems from different domains. However, it is necessary to critically validate these algorithms for solving real-world constrained optimization problems. The search behavior in those problems is different as it involves large number of linear, nonlinear and non-convex type equality and inequality constraints. In this work a 57 real-world constrained optimization problems test suite is solved using two constrained metaheuristic algorithms originated from a socio-based Cohort Intelligence (CI) algorithm. The first CI-based algorithm incorporates a self-adaptive penalty function approach i.e., CI-SAPF. The second algorithm combines CI-SAPF with the intrinsic properties of the physics-based Colliding Bodies Optimization (CBO) referred to CI-SAPF-CBO. The results obtained from CI-SAPF and CI-SAPF-CBO are compared with other constrained optimization algorithms. The superiority of the proposed algorithms is discussed in details followed by future directions to evolve the constrained handling techniques.
