An evolutionary algorithm for the solution of multi-objective optimization problem

<span>Worldwide, the COVID-19 widespread has significant impact on a great number of people. The hospital admittance issue for patients with been optimized by previous research. Identifying symptoms that can be used to determine patient's health status, whether they are dead or alive it is difficult task medical professionals. To solve this issue, multi-objective group counselling optimization (MOGCO) algorithm control problem. First, zitzler-deb-thiele (ZDT)-2 benchmark function evaluate MOGCO, particle swarm (MOPSO), and non dominated sorting genetic (NSGA) II. In comparison MOPSO NSGA-II, MOGCO closest Pareto front line according graphic statistics different fitness evolution values such as 4000, 6000, 8000, 10000. As result, data optimization. Moreover, six (heart rate, oxygen saturation, fever, body pain, flue, breath) were see if still alive. information was gathered from GitHub. Based minimum maximum these obtained suggested methodology, optimum study shows remain alive.</span>