Development of multi-objective equilibrium optimizer: application to cancer chemotherapy

Any multi-objective optimization algorithms are usually introduced by developing a single-objective algorithm. In the current study, a recently developed physics-based algorithm, equilibrium optimizer (EO), is regarded and is extended into a multi-objective algorithm called MOEO. The optimal control problem for cancer treatment is solved using this novel algorithm to confirm its performance. Considering the cancer treatment as an optimal control problem to reduce both the concentration of cancer cells and the concentration of the drugs during treatment is the multi-objective optimization problem of this research. Due to this, this problem’s solution leads to the optimal drug administration protocol to minimize cancer cell concentration and drug concentration. This multi-objective problem is solved using the novel MOEO algorithm, and the results are compared with the previous works. The Pareto front curve obtained from the algorithm offers a set of the most optimal solutions. According to the treatment selection criteria, one of these points is selected as the drug-prescribing protocol. The problem is solved based on three different case studies, where the result comparison shows that the MOEO’s performance is great. Considering different cost functions, it shows that using integral of the time-weighted absolute (ITA) objective function gets the fastest response with a higher drug dose while using integral of the absolute (IS) objective function gets lesser drugs.