A Fast Evaluation-Based Bacteria Colony Chemotaxis Algorithm for Dynamic Interval Multiobjective Optimization Problems

Abstract

There are many real-world applications with uncertainties that can be modeled as the dynamic interval multiobjective optimization problems (DI-MOPs). However, it is challenging for the traditional algorithms to converge rapidly before time-varying parameters change to obtain optimal solutions under interval objectives. So far, there is a lack of studies on the evaluation methods for interval optimal solutions in dynamic problems. Therefore, a fast evaluation framework is proposed in this article to tackle these issues. In this framework, we first derive a new hash function based on the Canberra distance and provide a theoretical proof of the validity and local sensitivity of the hash function, from which a Canberra locality sensitive hashing (CLSH) is constructed. The CLSH accelerates the search for interval evaluation objects in uncertain environments. Further, we propose an adaptive interval crowding distance (AICD) with relaxed constraints to obtain a global improvement in the quality of the solutions. The candidate solutions in the above framework are generated by the environment awareness and directed migration of the mutiobjective bacteria colony chemotaxis (MOBCC) algorithm. This complete algorithm is called the dynamic interval MOBCC (DI-MOBCC). In addition, the theoretical proofs of the validity and local sensitivity of hash functions are also provided. Computational results on the eight benchmark optimization problems and a path planning of the mobile robots in uncertain environments validate that the DI-MOBCC is more competitive than the other state of the art algorithms in tackling DI-MOPs.