Chaotic-SCA Salp Swarm Algorithm Enhanced with Opposition Based Learning: Application to Decrease Carbon Footprint in Patient Flow

Salp swarm algorithm is prone to problems such as a slow convergence rate and local optimal solution. To solve these problems, this chapter proposes the CSOSSA algorithm which integrates the sine cosine algorithm for updating the position of the leader, the chaotic maps are used for updating the position of followers, and opposition-based learning is used for a better exploration of the search space into the SSA. The CSOSSA is compared with the original SSA and other meta-heuristic algorithms on 13 benchmark functions of CEC2005 with unimodal or multimodal characteristics, and five De Jong's functions. The experimental results show that the performance of CSOSSA is better than or comparable with the SSA and other meta-heuristic algorithms. To show the applicability of the algorithm in real-world problems, then CSOSSA is applied to the green patient flow problem to minimize the total carbon emitted during the care process.