Multi-objective optimisation of cancer chemotherapy using smart PSO with decomposition

The paper presents a novel approach to optimising cancer chemotherapy with respect to conflicting treatment objectives aimed at reducing the number of cancerous cells and at limiting the amounts of anti-cancer drugs used. The approach is based on the Particle Swarm Optimisaion (PSO) algorithm that decomposes a multi-objective optimisation problem into several scalar aggregation problems, thereby reducing its complexity and enabling an effective application of Computational Intelligence techniques. The novelty of the algorithm is in providing particles in the swarm with information from a set of defined neighbours and leaders that assists in finding versatile chemotherapeutic treatments.

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