Generating properly efficient points in multi-objective programs by the nonlinear weighted sum scalarization method

Optimization ◽  
2012 ◽  
Vol 63 (3) ◽  
pp. 473-486 ◽  
Author(s):  
M. Zarepisheh ◽  
E. Khorram ◽  
Panos M. Pardalos
Keyword(s):  
Efficient Points ◽  
Weighted Sum ◽  
Multi Objective ◽  
Scalarization Method ◽  
Properly Efficient Points

scholarly journals A Linear Weighted Sum Multi-objective Optimization Algorithm Based on PSO for Wideband Spectrum Sensing

2015 ◽  
Vol 11 (9) ◽  
pp. 9 ◽  
Author(s):  
Yonghua Wang ◽  
Yuehong Li ◽  
Yiquan Zheng ◽  
Ting Liang ◽  
Yuli Fu
Keyword(s):  
Particle Swarm Optimization ◽  
Cognitive Radio ◽  
Sensor Networks ◽  
Spectrum Sensing ◽  
Weighted Sum ◽  
Multi Objective Optimization ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Cognitive Radio Sensor Networks ◽  
Wideband Spectrum Sensing

In order to maximize throughput and minimize interference of the wideband spectrum sensing problem in OFDM cognitive radio sensor networks, a linear weighted sum multi-objective algorithm based on the Particle Swarm Optimization is proposed. The multi-objective optimization advantages of Particle Swarm Optimization are utilized to solve the optimal threshold vector of the spectrum sensing problem in OFDM cognitive radio sensor networks. So the network can get a larger throughput under the condition of small interference. The simulation results show that the proposed algorithm can make larger throughput while keeping the interference is smaller in OFDM cognitive radio sensor networks. Thus the spectrum resources are used more effectively.

Study on Multi-Objective Reconstruction of Random Media

2016 ◽  
Vol 825 ◽  
pp. 153-160
Author(s):  
Adéla Hlobilová ◽  
Matěj Lepš
Keyword(s):  
Genetic Algorithm ◽  
Random Media ◽  
Probability Function ◽  
Volume Fraction ◽  
Weighted Sum ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Weighted Sum Method ◽  
Original Medium ◽  
Optimization Routine

This paper deals with a reconstruction of random media via multi-objective optimization. Two statistical descriptors, namely a two-point probability function and a two-point lineal path function, are repetitively evaluated for the original medium and the reconstructed image to appreciate the improvement in the optimization progress. Because of doubts of the weights setting in the weighted-sum method, purely multi-objective optimization routine Non-dominated Sorting Genetic Algorithm~II is utilized. Three operators are compared for creating new offspring populations that satisfy a prescribed volume fraction constraint. The main contribution is in the testing of the proposed methodology on several benchmark images.

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