نتایج جستجو برای: multi objectiveparticle swarm
تعداد نتایج: 485020 فیلتر نتایج به سال:
The formation of movement in a colony of animals in nature is as important as the movement itself. In transportation world or multi-agents system (swarm), like in military world, the formation of aircrafts or ships is also vital. This paper addresses the tracking situation of multi agents system that models swarming behavior moving along a desired path. This paper tries to model the movement of...
A swarm is a ‘‘complex adaptive system’’, which is decentralized and self-organized and whose individuals are simple, homogeneous and autonomous. Swarm intelligence is defined to describe its emergent behaviors. Both wireless sensor networks and mobile multi-robots demonstrate swarm features. This paper first discusses the challenges of combining wireless sensor networks and mobile multi-robots...
this paper proposes per unit coding for combined economic emission load dispatch problem. in the proposed coding, it is possible to apply the percent effects of elements in any number and with high accuracy in objective function. in the proposed per unit coding, each function is transformed into per unit form based on its own maximum value and has a value from 0 to 1. in this paper, particle sw...
A new multi-objective particle swarm optimization algorithm is presented. The proposed multi-objective particle swarm optimization algorithm is based on a MaxiMin technique previously proposed for a multi-objective genetic algorithm. The technique is applied to optimize a benchmark function set and to the design of PID controllers regarding the objectives of set-point tracking and output distur...
A novel orthogonal multi-swarm cooperative particle swarm optimization (PSO) algorithm with a particle trajectory knowledge base is presented in this paper. Different from the traditional PSO algorithms and other variants of PSO, the proposed orthogonal multi-swarm cooperative PSO algorithm not only introduces an orthogonal initialization mechanism and a particle trajectory knowledge base for m...
Multi-class miner is well recognized method for stream data classification. For the process of multi-class miner evaluation of new feature during classification is major problem. The problem of feature evaluation decreases the performance of multi-class miner (MCM). For the improvement of multi-class miner particle of swarm optimization technique is used. Particle of swarm optimization controls...
In this paper, we provide an overview of SwarmVis, a tool for visualizing swarm systems. SwarmVis integrates several visualization techniques to create informative still images and videos of swarm systems moving through twoand threedimensional space. SwarmVis allows swarm researchers to observe characteristics of swarm systems by visualizing information about such systems. We provide an overvie...
In this paper we propose neighborhood search (NS) assisted Swarm Intelligence (SI) technique for efficient Multi-input Multi-output (MIMO) receiver. The proposed NS-SI detection method iteratively decodes complex modulation schemes in MIMO system. The simulation results reveal that with this proposed NS-SI detection algorithm near optimal performance is achieved. The performance of proposed det...
Swarm robotics is an innovative approach to the control and coordination of a large number of multi-agent systems that use naturally inspired swarm intelligent methods to perform a task through emergent behaviors. A swarm based approach can decrease the complexity and the cost of designing a cooperative multi-robot system. This paper proposes a general engineering approach to develop a robotic ...
Evolutionary algorithm and Swarm Intelligence algorithm (EA, SI), a part of Bio inspired optimization algorithm, have been widely used to solve numerous optimization problem in various science and engineering domains. This paper proposes a Multi Swarm Particle Swarm Optimization (MS-PSO) algorithm inspired by the animal collective behavior, the movement of the swarm and the intelligence of the ...
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