نتایج جستجو برای: non dominated sorting genetic algorithm nsga ii

تعداد نتایج: 3021917  

Journal: :Appl. Soft Comput. 2016
R. Murugeswari S. Radhakrishnan D. Devaraj

The huge demand for real time services in wireless mesh networks (WMN) creates many challenging issues for providing quality of service (QoS). Designing of QoS routing protocols, which optimize the multiple objectives is computationally intractable. This paper proposes a new model for routing in WMN by using Modified Non-dominated Sorting Genetic Algorithm-II (MNSGA-II). The objectives which ar...

2010
Flávio Teixeira Alexandre R. S. Romariz

This chapter presents the application of a comprehensive statistical analysis for both algorithmic performance comparison and optimal parameter estimation on a multi-objective digital signal processing problem. The problem of designing optimum digital finite impulse response (FIR) filters with the simultaneous approximation of the filter magnitude and phase is posed as a multiobjective optimiza...

The aim of a multi-mode resource-constrained project scheduling problem (MRCPSP) is to assign resource(s) with the restricted capacity to an execution mode of activities by considering relationship constraints, to achieve pre-determined objective(s). These goals vary with managers or decision makers of any organization who should determine suitable objective(s) considering organization strategi...

Journal: :Processes 2023

As a new model of networked manufacturing services, cloud (CMfg) aims to allocate enterprise resources, realize rational utilization and adapt increasingly complex user needs. However, previous studies on service composition optimal selection (SCOS) in CMfg environments do not incorporate carbon emissions into the quality (QoS) evaluation indicators. Therefore, SCOS for under low-carbon environ...

2017
Luis Martí Eduardo Segredo Nayat Sánchez Pi Emma Hart

Selection methods are a key component of all multi-objective and, consequently, many-objective optimisation evolutionary algorithms. They must perform two main tasks simultaneously. First of all, they must select individuals that are as close as possible to the Pareto optimal front (convergence). Second, but not less important, they must help the evolutionary approach to provide a diverse popul...

Journal: :journal of optimization in industrial engineering 2010
seyed hamid reza pasandideh amirhossain chambari

this paper proposes a bi-objective model for the facility location problem under a congestion system. the idea of the model is motivated by applications of locating servers in bank automated teller machines (atms), communication networks, and so on. this model can be specifically considered for situations in which fixed service facilities are congested by stochastic demand within queueing frame...

2017
Maryam Ghasemi Ali Farzan

Planning and scheduling are as decision making processes which they have important roles in production systems and industries. According that, job shop scheduling is one of NPhard problems to solve multi-objective decision making approaches. So, the problem is known as uncertain with many variables in optimal solution view. Finding optimal solutions are essential task in scheduling of jobs betw...

2008
M. Jagadeeswari M. C. Bhuvaneswari

This paper proposes a novel Multi-Objective Evolutionary Algorithm for hardware software partitioning of embedded systems. Customized genetic algorithms (GA) have been effectively used for solving complex optimization problems (NP Hard) but are mainly applied to optimize a particular solution with respect to a single objective. Many real world problems in embedded systems have multiple objectiv...

2005
Yue Li Gade P. Rangaiah Ajay Kumar Ray

Optimization of industrial styrene reactor design for two objectives using the non-dominated sorting genetic algorithm (NSGA) is studied. Both adiabatic and steam-injected reactors are considered. The two objectives are maximization of styrene production and styrene selectivity. The study shows that styrene reactor design can be optimized easily and reliably for two objectives by NSGA. It provi...

2004
Antony W. Iorio Xiaodong Li

The following paper describes a cooperative coevolutionary algorithm which incorporates a novel collaboration formation mechanism. It encourages rewarding of components participating in successful collaborations from each sub-population. The successfulness of the collaboration is measured by a non-dominated sorting procedure. The algorithm has demonstrated it can perform comparably with the NSG...

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