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Multi-Objective Optimization of Cancer Chemotherapy Using Swarm Intelligence

Cancer chemotherapy aims at achieving a number of treatment goals, not all of which are commensurate. Because of this, the problem of chemotherapy optimization necessitates the use of multi-objective optimization methods. The techniques based on swarm intelligence have certain features that make them applicable and effective in addressing multiple treatment objectives of cancer chemotherapy. This paper demonstrates the adaptive capabilities of particle swarm optimization (PSO) that enables this bio-inspired metaheuristic to carry out an efficient search for both effective and versatile chemotherapy treatments. of the tumour reduction criterion - that is, how effectively they reduce the overall tumour burden during the treatment period. Although tumour reduction, and ideally elimination, remains the primary objective of cancer chemotherapy, its achievement is not always possible due to various medical constraints. It is essential therefore to provide oncologists with the option to change the objective of treatment optimization from finding the best curative treatment to developing a good controlling (palliative) treatment capable of extending the patient survival time (PST) or the quality of patients' lives. The authors have successfully explored the possibility of multi- objective optimization of cancer chemotherapy in their previous work (7), but confined their study to the domain of genetic algorithms only. Given inferior performance of this optimization technique in comparison with particle swarm optimization (PSO) and estimation of distribution algorithms (EDAs), it seems logical to revive the multi-objective approach to cancer chemotherapy optimization in the context of more efficient heuristics with the purpose to enhance the optimization facility of the developed decision support system for oncologists.