The problem of chemotherapy treatment optimization can be defined in order to minimize the size of the tumor without endangering the patient's health; therefore, chemotherapy requires to achieve a number of objectives, simultaneously. For this reason, the optimization problem turns to a multi-objective problem. In this paper, a multi-objective meta-heuristic method is provided for cancer chemotherapy with the aim of balancing between two objectives: the amount of toxicity and the number of cancerous cells. The proposed method uses mathematical models in order to measure the drug concentration, tumor growth and the amount of toxicity. This method utilizes a Multi-Objective Particle Swarm Optimization (MOPSO) algorithm to optimize cancer chemotherapy plan using cell-cycle specific drugs. The proposed method can be a good model for personalized medicine as it returns a set of solutions as output that have balanced between different objectives and provided the possibility to choose the most appropriate therapeutic plan based on some information about the status of the patient. Experimental results confirm that the proposed method is able to explore the search space efficiently in order to find out the suitable treatment plan with minimal side effects. This main objective is provided using a desirable designing of chemotherapy drugs and controlling the injection dose. Moreover, results show that the proposed method achieve to a better therapeutic performance compared to a more recent similar method [1].

翻译：化疗治疗优化问题可定义为在不危及患者健康的前提下最小化肿瘤尺寸，因此化疗需要同时达成多个目标。基于此，该优化问题转化为多目标问题。本文提出一种多目标元启发式方法用于癌症化疗，旨在平衡两个目标：毒性剂量与癌细胞数量。所提方法采用数学模型测量药物浓度、肿瘤生长及毒性剂量。该方法利用多目标粒子群优化（MOPSO）算法，基于细胞周期特异性药物优化化疗方案。由于输出一组在不同目标间达成平衡的解集，并根据患者状态信息提供选择最适治疗方案的灵活性，所提方法可作为个性化医疗的良好模型。实验结果证实，该方法能有效探索搜索空间以发现副作用最小的适宜治疗方案，这一主要目标通过合理设计化疗药物并控制注射剂量得以实现。此外，结果表明与近期同类方法[1]相比，所提方法取得了更优的治疗效果。