The improvement research on multi-objective optimization algorithm based on non-dominated sorting

多目标优化问题（MOP）在许多科学研究和工程设计当中普遍存在，此类问题求解十分复杂但又十分重要。尽管传统多目标优化算法已经有了长足的发展，但遗存的问题依然很多，需要改进。 进化多目标优化算法将传统方法中的加权策略改为以种群为单位的进化策略，取得了更理想的优化的效果，NSGA-II就是其中的佼佼者。在此次研究中本人在NSGA-II的基础上提出了一种基于随机交叉算子、变异算子的算法RCVO-NSGA-II（RandomCrossVariationOperator-nondominatedsortinggeneticalgorithmII）用于解多目标优化问题。RCVO-NSGA-II随机采用模拟...Multiobjective optimization problem is common existing in many scientific researches and engineering design and the solution of this kind of problem is very complicated and important. Although the development of the traditional multi-objective optimization algorithm have made great progress, but a lot of problems are need to be improved. Evolutionary multi-objective optimization algorithm change ...学位：工程硕士院系专业：信息科学与技术学院_工程硕士(计算机技术)学号：X201222101

Therapeutic and Nutritional Potential of Spirulina in Combating COVID-19 Infection

Human history has witnessed various pandemics throughout, and these cause disastrous effects on human health and country’s economy. Once again, after SARS (Severe Acute Respiratory Syndrome) and MERS (Middle East Respiratory Syndrome), the world is observing a very tough time fighting an invisible enemy, the novel COVID-19 coronavirus. Initially observed in the Wuhan province of China, now, it has spread across 210 countries. Number of corona affected confirmed cases have reached > 3 million globally and death toll has reached to 258,481 as on 6th May,2020. Researchers are working round the clock, forming collaborative efforts and sharing their data to come up with a cure for this disease. The new coronavirus genome was quickly sequenced and clinical and epidemiological data are continuously being collected and analyzed. This data is crucial for forming better public health policies and developing antiviral drugs and vaccines. As there is no vaccine available in market against COVID-19, personal health, immunity, social distancing and basic protection measures are extremely important. It is critical to avoid the virus infection and to strengthen the immune system as the coronavirus can be fatal for those with weak immunity.  This article reviews the nutritional and therapeutic potential of Spirulina, which is considered as superfood and a natural supplement to strengthen the immune system. Spirulina is highly nutritious and has hypolipidemic, hypoglycemic and antihypertensive properties. Spirulina contains several bioactive compounds, such as phenols, phycobiliproteins and sulphated polysaccharides and many more with proven antioxidant, anti-inflammatory and immunostimulant/ immunomodulatory effects

A New Multi-Objective Ant Colony Optimisation Algorithm for Solving the Quadratic Assignment Problem

The multi-objective quadratic assignment problem (mQAP) is an NP-hard combinatorial optimisation problem. Real world problems are concerned with multi-objective problems which optimise more objective functions simultaneously. Moreover, QAP models many real-world optimisation problems, such as network design problems, communication problems, layout problems, etc. One of its major applications is the facility location, which is to find an assignment of all facilities to all locations in the way their total is minimised. The multi-objective QAP considers multiple types of flows between two facilities. Over the last few decades several meta-heuristic algorithms have been proposed to solve the multi-objective QAP, such as genetic algorithms, Tabu search, simulated annealing, and ant colony optimisation. This paper presents a new ant colony optimisation algorithm for solving multiple objective optimisation problems, and it is named as the random weight-based ant colony optimisation algorithm (RWACO). The proposed algorithm is applied to the bi-objective quadratic assignment problem and evaluates the performance by comparing with some recently developed multiobjective ant colony optimisation algorithms. The experimental results have shown that the proposed algorithm performs better than the other multi-objective ACO algorithms considered in this study.Keywords: ACO, multi-objective problem, QAP, travelling salesman proble

A Perturbed Self-organizing Multiobjective Evolutionary Algorithm to solve Multiobjective TSP

Travelling Salesman Problem (TSP) is a very important NP-Hard problem getting focused more on these days. Having improvement on TSP, right now consider the multi-objective TSP (MOTSP), broadened occurrence of travelling salesman problem. Since TSP is NP-hard issue MOTSP is additionally a NP-hard issue. There are a lot of algorithms and methods to solve the MOTSP among which Multiobjective evolutionary algorithm based on decomposition is appropriate to solve it nowadays. This work presents a new algorithm which combines the Data Perturbation, Self-Organizing Map (SOM) and MOEA/D to solve the problem of MOTSP, named Perturbed Self-Organizing multiobjective Evolutionary Algorithm (P-SMEA). In P-SMEA Self-Organizing Map (SOM) is used extract neighborhood relationship information and with MOEA/D subproblems are generated and solved simultaneously to obtain the optimal solution. Data Perturbation is applied to avoid the local optima. So by using the P-SMEA, MOTSP can be handled efficiently. The experimental results show that P-SMEA outperforms MOEA/D and SMEA on a set of test instances

Real-Time Gate Reassignment Based on Flight Delay Feature in Hub Airport

Appropriate gate reassignment is crucially important in efficiency improvement on airport sources and service quality of travelers. The paper divides delay flight into certain delay time flight and uncertain delay time flight based on flight delay feature. The main objective functions of model are to minimize the disturbance led by gate reassignment in the case of certain delay time flight and uncertain delay time flight, respectively. Another objective function of model is to build penalty function when the gate reassignment of certain delay time flight influences uncertain delay time flight. Ant colony algorithm (ACO) is presented to simulate and verify the effectiveness of the model. The comparison between simulation result and artificial assignment shows that the result coming from ACO is obvious prior to the result coming from artificial assignment. The maximum disturbance of gate assignment is decreased by 13.64%, and the operation time of ACO is 118 s. The results show that the strategy of gate reassignment is feasible and effective

Repairing the Inconsistent Fuzzy Preference Matrix Using Multiobjective PSO

This paper presents a method using multiobjective particle swarm optimization (PSO) approach to improve the consistency matrix in analytic hierarchy process (AHP), called PSOMOF. The purpose of this method is to optimize two objectives which conflict each other, while improving the consistency matrix. They are minimizing consistent ratio (CR) and deviation matrix. This study focuses on fuzzy preference matrix as one model comparison matrix in AHP. Some inconsistent matrices are repaired successfully to be consistent by this method. This proposed method offers some alternative consistent matrices as solutions

Multi-objective ant colony optimization for the twin-screw configuration problem

The Twin-Screw Configuration Problem (TSCP) consists in identifying the best location of a set of available screw elements along a screw shaft. Due to its combinatorial nature, it can be seen as a sequencing problem. In addition, different conflicting objectives may have to be considered when defining a screw configuration and, thus, it is usually tackled as a multi-objective optimization problem. In this research, a multi-objective ant colony optimization (MOACO) algorithm was adapted to deal with the TSCP. The influence of different parameters of the MOACO algorithm was studied and its performance was compared with that of a previously proposed multi-objective evolutionary algorithm and a two-phase local search algorithm. The experimental results showed that MOACO algorithms have a significant potential for solving the TSCP.This work has been supported by the Portuguese Fundacao para a Ciencia e Tecnologia under PhD grant SFRH/BD/21921/2005. Thomas Stutzle acknowledges support of the Belgian F.R.S-FNRS of which he is a research associate, the E-SWARM project, funded by an ERC Advanced Grant, and by the Meta-X project, funded by the Scientific Research Directorate of the French Community of Belgium