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

Generalized multiobjective evolutionary algorithm guided by descent directions

This paper proposes a generalized descent directions-guided multiobjective algorithm (DDMOA2). DDMOA2 uses the scalarizing fitness assignment in its parent and environmental selection procedures. The population consists of leader and non-leader individuals. Each individual in the population is represented by a tuple containing its genotype as well as the set of strategy parameters. The main novelty and the primary strength of our algorithm is its reproduction operator, which combines the traditional local search and stochastic search techniques. To improve efficiency, when the number of objective is increased, descent directions are found only for two randomly chosen objectives. Furthermore, in order to increase the search pressure in high-dimensional objective space, we impose an additional condition for the acceptance of descent directions found for leaders during local search. The performance of the proposed approach is compared with those produced by representative state-of-the-art multiobjective evolutionary algorithms on a set of problems with up to 8 objectives. The experimental results reveal that our algorithm is able to produce highly competitive results with well-established multiobjective optimizers on all tested problems.Moreover, due to its hybrid reproduction operator, DDMOA2 demonstrates superior performance on multimodal problems.This work has been supported by FCT Fundação para a Ciência e Tecnologia in the scope of the project: PEst-OE/EEI/UI0319/2014

Sub-graph based Multicast Protection in WDM Networks: A Multi/Many-Objective Evolutionary Algorithms approaches

In this paper is addressed the multicast routing-and-protection, and wavelength assignment (MRPWA) problem which is critical for the success of applications point-multipoint in WDM networks. Basically, it is proposed the design of the primary and protection multicast routes, where the resources protection are based on sub-graph protection strategy subject to the quality requirements of the QoP protection: dedicated (1 + 1), shared (M: N) and better effort (without protection). In this way, NSGA-II and NSGA-III, evolutionary algorithms, are applied to MRPWA considering multi- and many-objectives optimization context, respectively. The evolutionary algorithms optimize simultaneously: (i) the total number of links used, (ii) the number of wavelength converters, (iii) the number of splitter nodes, and (iv) the number of destinations served-and-protected. Considering Hyper-volume measure, the experimental tests on a set of instances indicate that the protection approach based on sub-graph proves to be promising in comparison to the dualtree protection strategy. On the other hand, the evolutionary technique oriented to many-objectives (NSGA-III) is more convenient than the oriented towards multi-objectives (NSGA-II) in the study problem.XIII Workshop Arquitectura, Redes (WARSO)Red de Universidades con Carreras en Informática (RedUNCI

Sub-graph based Multicast Protection in WDM Networks: A Multi/Many-Objective Evolutionary Algorithms approaches

In this paper is addressed the multicast routing-and-protection, and wavelength assignment (MRPWA) problem which is critical for the success of applications point-multipoint in WDM networks. Basically, it is proposed the design of the primary and protection multicast routes, where the resources protection are based on sub-graph protection strategy subject to the quality requirements of the QoP protection: dedicated (1 + 1), shared (M: N) and better effort (without protection). In this way, NSGA-II and NSGA-III, evolutionary algorithms, are applied to MRPWA considering multi- and many-objectives optimization context, respectively. The evolutionary algorithms optimize simultaneously: (i) the total number of links used, (ii) the number of wavelength converters, (iii) the number of splitter nodes, and (iv) the number of destinations served-and-protected. Considering Hyper-volume measure, the experimental tests on a set of instances indicate that the protection approach based on sub-graph proves to be promising in comparison to the dualtree protection strategy. On the other hand, the evolutionary technique oriented to many-objectives (NSGA-III) is more convenient than the oriented towards multi-objectives (NSGA-II) in the study problem.XIII Workshop Arquitectura, Redes (WARSO)Red de Universidades con Carreras en Informática (RedUNCI

Convex hull ranking algorithm for multi-objective evolutionary algorithms

AbstractDue to many applications of multi-objective evolutionary algorithms in real world optimization problems, several studies have been done to improve these algorithms in recent years. Since most multi-objective evolutionary algorithms are based on the non-dominated principle, and their complexity depends on finding non-dominated fronts, this paper introduces a new method for ranking the solutions of an evolutionary algorithm’s population. First, we investigate the relation between the convex hull and non-dominated solutions, and discuss the complexity time of the convex hull and non-dominated sorting problems. Then, we use convex hull concepts to present a new ranking procedure for multi-objective evolutionary algorithms. The proposed algorithm is very suitable for convex multi-objective optimization problems. Finally, we apply this method as an alternative ranking procedure to NSGA-II for non-dominated comparisons, and test it using some benchmark problems

On the evolutionary optimisation of many conflicting objectives

This inquiry explores the effectiveness of a class of modern evolutionary algorithms, represented by Non-dominated Sorting Genetic Algorithm (NSGA) components, for solving optimisation tasks with many conflicting objectives. Optimiser behaviour is assessed for a grid of mutation and recombination operator configurations. Performance maps are obtained for the dual aims of proximity to, and distribution across, the optimal trade-off surface. Performance sweet-spots for both variation operators are observed to contract as the number of objectives is increased. Classical settings for recombination are shown to be suitable for small numbers of objectives but correspond to very poor performance for higher numbers of objectives, even when large population sizes are used. Explanations for this behaviour are offered via the concepts of dominance resistance and active diversity promotion