A Vascular Invasive Tumor Growth Optimization Algorithm for Multi-Objective Optimization

Abstract

Multi-objective optimization problems (MOPs) have received much attention in recent years. To deal with these problems, many multi-objective optimization algorithms have been proposed, especially the heuristic algorithms. In this paper, we proposed a multi-objective optimization algorithm called vascular invasive tumor growth optimization (VITGO), which based on the invasive tumor growth optimization and utilized a vascular mechanism to solve the MOPs. The newly proposed algorithm contains two parts: the vascular units and tumor cells. The former ones are utilized to record the Pareto solutions of the MOPs and define the search direction, and the latter ones are utilized to cooperate with vascular units to search deeper and wider. The mechanisms in the VITGO algorithm includes: endpoint generation, approximate Pareto front guidance, opposite searching, and adaptively detailed searching. Experiments showed that compared with other state-of-the-art multi-objective optimization algorithms, VITGO performs better in convergence and diversity.

Toward Identifying Key Gene Group in the Occurrence and Development of Lung Adenocarcinoma

“…As cancers involve complex pathogenic factors and huge molecular networks, it is difficult to find molecular targets to achieve the expected therapeutic effect [5]. There have been many studies focusing on cancer research, including but not limited to cancer metabolic reprogramming, cell autophagy, cell apoptosis, stem cells, cell cycle, angiogenesis [6], [7], cell adhesion, immunity and vaccines, microenvironment, epigenetic regulation, and even intestinal flora [8]. Many studies about cancers have analyzed the specific mechanisms of tumorigenesis, and most cancer researchers hope to develop new drugs for curing or even conquering a certain type of tumor through the exploration of mechanisms.…”