نتایج جستجو برای: mosa and mopso algorithm

تعداد نتایج: 16906653  

Journal: :European Journal of Operational Research 2010
Chi Keong Goh Kay Chen Tan D. S. Liu Swee Chiang Chiam

Multi-objective particle swarm optimization (MOPSO) is an optimization technique inspired by bird flocking, which has been steadily gaining attention from the research community because of its high convergence speed. On the other hand, in the face of increasing complexity and dimensionality of today’s application coupled with its tendency of premature convergence due to the high convergence spe...

Journal: :JCS 2016
Qusay Omran Mosa Mohammad Faidzul Nasrudin

Corresponding Author: Qusay Omran Mosa Faculty of Information Science and Technology, University Kebangsaan Malaysia, Malaysia Email: [email protected] Abstract: Active contours also known as snakes became a familiar and widely used in the field of image segmentation and restoration of historical documents in last few decades. Gradient Vector Flow (GVF) snake successes in overcome of converge...

Journal: :Electronics 2023

With the rapid development of sensor technology and mobile services, service model crowd sensing (MCS) has emerged. In this model, user groups perceive data through carried terminal devices, thereby completing large-scale distributed tasks. Task allocation is an important link in MCS, but interests task publishers, users, platforms often conflict. Therefore, to improve performance MCS allocatio...

A mobile ad-hoc network (MANET), a set of wirelessly connected sensor nodes, is a dynamic system that executes hop-by-hop routing independently with no external help of any infrastructure. Proper selection of cluster heads can increase the life time of the Ad-hoc network by decreasing the energy consumption. Although different methods have been successfully proposed by researchers to tackle...

2013
S. LALWANI Alireza Abdollahi

Numerous problems encountered in real life cannot be actually formulated as a single objective problem; hence the requirement of Multi-Objective Optimization (MOO) had arisen several years ago. Due to the complexities in such type of problems powerful heuristic techniques were needed, which has been strongly satisfied by Swarm Intelligence (SI) techniques. Particle Swarm Optimization (PSO) has ...

2017
Rui Zhang

The traditional way of scheduling production processes often focuses on profit-driven goals (such as cycle time or material cost) while tending to overlook the negative impacts of manufacturing activities on the environment in the form of carbon emissions and other undesirable by-products. To bridge the gap, this paper investigates an environment-aware production scheduling problem that arises ...

A mobile ad-hoc network (MANET), a set of wirelessly connected sensor nodes, is a dynamic system that executes hop-by-hop routing independently with no external help of any infrastructure. Proper selection of cluster heads can increase the life time of the Ad-hoc network by decreasing the energy consumption. Although different methods have been successfully proposed by researchers to tackle...

Journal: :Applied sciences 2023

With the continuous increase in emphasis on environmental protection industry, sewage treatment plants have been built many places, and these undoubtedly occupy an important position protecting local environment. The process is generally complicated environment difficult, which means that plant must excellent control system. At this stage, systems cities issue of possessing backward technology ...

Journal: :IOP Conference Series: Earth and Environmental Science 2020

Journal: :Swarm and Evolutionary Computation 2016
Sudhansu Kumar Mishra Ganapati Panda Babita Majhi

In this paper, a novel prediction based mean-variance (PBMV) model has been proposed, as an alternative to the conventional Markowitz mean-variance model, to solve the constrained portfolio optimization problem. In the Markowitz mean-variance model, the expected future return is taken as the mean of the past returns, which is incorrect. In the proposed model, first the expected future returns a...

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