On the Computational Effectiveness of Multiple Objective Metaheuristics

2002 ◽  
pp. 86-100 ◽  
Author(s):  
Andrzej Jaszkiewicz
Keyword(s):  
Multiple Objective

Multiple objective optimisation in a behavioural synthesis system

1993 ◽  
Vol 140 (4) ◽  
pp. 253 ◽  
Author(s):  
K.R. Baker ◽  
A.J. Currie ◽  
K.G. Nichols
Keyword(s):  
Multiple Objective ◽  
Synthesis System ◽  
Multiple Objective Optimisation

scholarly journals System Dynamics-Multiple Objective Optimization Model for Water Resource Management: A Case Study in Jiaxing City, China

Water ◽  
2021 ◽  
Vol 13 (5) ◽  
pp. 671
Author(s):  
Xiaoying Zhou ◽  
Feier Wang ◽  
Kuan Huang ◽  
Huichun Zhang ◽  
Jie Yu ◽  
...  
Keyword(s):  
Resource Management ◽  
System Dynamics ◽  
Water Resource ◽  
Water Allocation ◽  
Water Resource Management ◽  
Water Shortage ◽  
Multiple Objective ◽  
Multiple Objective Optimization ◽  
Model Framework

Predicting and allocating water resources have become important tasks in water resource management. System dynamics and optimal planning models are widely applied to solve individual problems, but are seldom combined in studies. In this work, we developed a framework involving a system dynamics-multiple objective optimization (SD-MOO) model, which integrated the functions of simulation, policy control, and water allocation, and applied it to a case study of water management in Jiaxing, China to demonstrate the modeling. The predicted results of the case study showed that water shortage would not occur at a high-inflow level during 2018–2035 but would appear at mid- and low-inflow levels in 2025 and 2022, respectively. After we made dynamic adjustments to water use efficiency, economic growth, population growth, and water resource utilization, the predicted water shortage rates decreased by approximately 69–70% at the mid- and low-inflow levels in 2025 and 2035 compared to the scenarios without any adjustment strategies. Water allocation schemes obtained from the “prediction + dynamic regulation + optimization” framework were competitive in terms of social, economic and environmental benefits and flexibly satisfied the water demands. The case study demonstrated that the SD-MOO model framework could be an effective tool in achieving sustainable water resource management.

scholarly journals A Method for Integration of Preferences to a Multi-Objective Evolutionary Algorithm Using Ordinal Multi-Criteria Classification

2021 ◽  
Vol 26 (2) ◽  
pp. 27
Author(s):  
Alejandro Castellanos-Alvarez ◽  
Laura Cruz-Reyes ◽  
Eduardo Fernandez ◽  
Nelson Rangel-Valdez ◽  
Claudia Gómez-Santillán ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Selective Pressure ◽  
Optimization Problems ◽  
Region Of Interest ◽  
Decision Makers ◽  
Multiple Objective ◽  
Objective Functions ◽  
Multi Objective ◽  
Imperfect Knowledge ◽  
Real World Problems

Most real-world problems require the optimization of multiple objective functions simultaneously, which can conflict with each other. The environment of these problems usually involves imprecise information derived from inaccurate measurements or the variability in decision-makers’ (DMs’) judgments and beliefs, which can lead to unsatisfactory solutions. The imperfect knowledge can be present either in objective functions, restrictions, or decision-maker’s preferences. These optimization problems have been solved using various techniques such as multi-objective evolutionary algorithms (MOEAs). This paper proposes a new MOEA called NSGA-III-P (non-nominated sorting genetic algorithm III with preferences). The main characteristic of NSGA-III-P is an ordinal multi-criteria classification method for preference integration to guide the algorithm to the region of interest given by the decision-maker’s preferences. Besides, the use of interval analysis allows the expression of preferences with imprecision. The experiments contrasted several versions of the proposed method with the original NSGA-III to analyze different selective pressure induced by the DM’s preferences. In these experiments, the algorithms solved three-objectives instances of the DTLZ problem. The obtained results showed a better approximation to the region of interest for a DM when its preferences are considered.

scholarly journals Different Object Functions for SWIPT Optimization by SADDE and APSO

Symmetry ◽  
2021 ◽  
Vol 13 (8) ◽  
pp. 1340
Author(s):  
Wei Chien ◽  
Chien-Ching Chiu ◽  
Po-Hsiang Chen ◽  
Yu-Ting Cheng ◽  
Eng Hock Lim ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Antenna Array ◽  
Millimeter Wave ◽  
Bit Error Rate ◽  
Weighting Factor ◽  
Multiple Objective ◽  
The Novel ◽  
Object Function ◽  
Swarm Optimization ◽  
Multiple Receivers

Multiple objective function with beamforming techniques by algorithms have been studied for the Simultaneous Wireless Information and Power Transfer (SWIPT) technology at millimeter wave. Using the feed length to adjust the phase for different objects of SWIPT with Bit Error Rate (BER) and Harvesting Power (HP) are investigated in the broadband communication. Symmetrical antenna array is useful for omni bearing beamforming adjustment with multiple receivers. Self-Adaptive Dynamic Differential Evolution (SADDE) and Asynchronous Particle Swarm Optimization (APSO) are used to optimize the feed length of the antenna array. Two different object functions are proposed in the paper. The first one is the weighting factor multiplying the constraint BER and HP plus HP. The second one is the constraint BER multiplying HP. Simulations show that the first object function is capable of optimizing the total harvesting power under the BER constraint and APSO can quickly converges quicker than SADDE. However, the weighting for the final object function requires a pretest in advance, whereas the second object function does not need to set the weighting case by case and the searching is more efficient than the first one. From the numerical results, the proposed criterion can achieve the SWIPT requirement. Thus, we can use the novel proposed criterion (the second criterion) to optimize the SWIPT problem without testing the weighting case by case.

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