scholarly journals Optimal Microgrid Topology Design and Siting of Distributed Generation Sources Using a Multi-Objective Substrate Layer Coral Reefs Optimization Algorithm

2018 ◽  
Vol 11 (1) ◽  
pp. 169 ◽  
Author(s):  
Silvia Jiménez-Fernández ◽  
Carlos Camacho-Gómez ◽  
Ricardo Mallol-Poyato ◽  
Juan Fernández ◽  
Javier Del Ser ◽  
...  
Keyword(s):  
Renewable Energy ◽  
Coral Reefs ◽  
Optimization Algorithm ◽  
Optimization Techniques ◽  
Topology Design ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Conflicting Objectives ◽  
Coral Reefs Optimization Algorithm ◽  
Photovoltaic Generators

In this work, a problem of optimal placement of renewable generation and topology design for a Microgrid (MG) is tackled. The problem consists of determining the MG nodes where renewable energy generators must be optimally located and also the optimization of the MG topology design, i.e., deciding which nodes should be connected and deciding the lines’ optimal cross-sectional areas (CSA). For this purpose, a multi-objective optimization with two conflicting objectives has been used, utilizing the cost of the lines, C, higher as the lines’ CSA increases, and the MG energy losses, E, lower as the lines’ CSA increases. To characterize generators and loads connected to the nodes, on-site monitored annual energy generation and consumption profiles have been considered. Optimization has been carried out by using a novel multi-objective algorithm, the Multi-objective Substrate Layers Coral Reefs Optimization algorithm (Mo-SL-CRO). The performance of the proposed approach has been tested in a realistic simulation of a MG with 12 nodes, considering photovoltaic generators and micro-wind turbines as renewable energy generators, as well as the consumption loads from different commercial and industrial sites. We show that the proposed Mo-SL-CRO is able to solve the problem providing good solutions, better than other well-known multi-objective optimization techniques, such as NSGA-II or multi-objective Harmony Search algorithm.

Effective multi-objective optimization with the coral reefs optimization algorithm

2015 ◽  
Vol 48 (6) ◽  
pp. 966-984 ◽  
Author(s):  
S. Salcedo-Sanz ◽  
A. Pastor-Sánchez ◽  
J. A. Portilla-Figueras ◽  
L. Prieto
Keyword(s):  
Coral Reefs ◽  
Optimization Algorithm ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Coral Reefs Optimization Algorithm

Efficient Computation of Probabilistic Dominance in Multi-objective Optimization

2021 ◽  
Vol 1 (4) ◽  
pp. 1-26
Author(s):  
Faramarz Khosravi ◽  
Alexander Rass ◽  
Jürgen Teich
Keyword(s):  
Optimization Problems ◽  
Statistical Tests ◽  
Optimization Techniques ◽  
Simultaneous Optimization ◽  
Sampled Data ◽  
Efficient Computation ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Conflicting Objectives ◽  
Comparison Operator

Real-world problems typically require the simultaneous optimization of multiple, often conflicting objectives. Many of these multi-objective optimization problems are characterized by wide ranges of uncertainties in their decision variables or objective functions. To cope with such uncertainties, stochastic and robust optimization techniques are widely studied aiming to distinguish candidate solutions with uncertain objectives specified by confidence intervals, probability distributions, sampled data, or uncertainty sets. In this scope, this article first introduces a novel empirical approach for the comparison of candidate solutions with uncertain objectives that can follow arbitrary distributions. The comparison is performed through accurate and efficient calculations of the probability that one solution dominates the other in terms of each uncertain objective. Second, such an operator can be flexibly used and combined with many existing multi-objective optimization frameworks and techniques by just substituting their standard comparison operator, thus easily enabling the Pareto front optimization of problems with multiple uncertain objectives. Third, a new benchmark for evaluating uncertainty-aware optimization techniques is introduced by incorporating different types of uncertainties into a well-known benchmark for multi-objective optimization problems. Fourth, the new comparison operator and benchmark suite are integrated into an existing multi-objective optimization framework that features a selection of multi-objective optimization problems and algorithms. Fifth, the efficiency in terms of performance and execution time of the proposed comparison operator is evaluated on the introduced uncertainty benchmark. Finally, statistical tests are applied giving evidence of the superiority of the new comparison operator in terms of \epsilon -dominance and attainment surfaces in comparison to previously proposed approaches.

A Thompson Sampling Efficient Multi-Objective Optimization Algorithm (TSEMO) for Lithium-Ion Battery Liquid-Cooled Thermal Management System: Study of Hydrodynamic, Thermodynamic, and Structural Performance

2020 ◽  
Vol 18 (2) ◽  
Author(s):  
A. Garg ◽  
Cheng Liu ◽  
A. K. Jishnu ◽  
Liang Gao ◽  
My Loan Le Phung ◽  
...  
Keyword(s):  
Thermal Management ◽  
Optimization Algorithm ◽  
Cooling System ◽  
Hydrodynamic Performance ◽  
Design Parameters ◽  
Inlet Temperature ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Thompson Sampling ◽  
Conflicting Objectives

Abstract The efficient design of battery thermal management systems (BTMSs) plays an important role in enhancing the performance, life, and safety of electric vehicles (EVs). This paper aims at designing and optimizing cold plate-based liquid cooling BTMS. Pitch sizes of channels, inlet velocity, and inlet temperature of the outermost channel are considered as design parameters. Evaluating the influence and optimization of design parameters by repeated computational fluid dynamics calculations is time consuming. To tackle this, the effect of design parameters is studied by using surrogate modeling. Optimized design variables should ensure a perfect balance between certain conflicting goals, namely, cooling efficiency, BTMS power consumption (parasitic power), and size of the battery. Therefore, the optimization problem is decoupled into hydrodynamic performance, thermodynamic performance, and mechanical structure performance. The optimal design involving multiple conflicting objectives in BTMS is solved by adopting the Thompson sampling efficient multi-objective optimization algorithm. The results obtained are as follows. The optimized average battery temperature after optimization decreased from 319.86 K to 319.2759 K by 0.18%. The standard deviation of battery temperature decreased from 5.3347 K to 5.2618 K by 1.37%. The system pressure drop decreased from 7.3211 Pa to 3.3838 Pa by 53.78%. The performance of the optimized battery cooling system has been significantly improved.

Multi-Objective Optimization of Piezoelectric Actuator Placement for Shape Control of Plates Using Genetic Algorithms

2009 ◽  
Vol 131 (9) ◽  
Author(s):  
Rajesh Kudikala ◽  
Deb Kalyanmoy ◽  
Bishakh Bhattacharya
Keyword(s):  
Optimization Algorithm ◽  
Shape Control ◽  
Optimization Problem ◽  
Piezoelectric Actuators ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Input Control ◽  
Wide Range ◽  
Conflicting Objectives ◽  
Lag Model

Shape control of adaptive structures using piezoelectric actuators has found a wide range of applications in recent years. In this paper, the problem of finding optimal distribution of piezoelectric actuators and corresponding actuation voltages for static shape control of a plate is formulated as a multi-objective optimization problem. The two conflicting objectives considered are minimization of input control energy and minimization of mean square deviation between the desired and actuated shapes with constraints on the maximum number of actuators and maximum induced stresses. A shear lag model of the smart plate structure is created, and the optimization problem is solved using an evolutionary multi-objective optimization algorithm: nondominated sorting genetic algorithm-II. Pareto-optimal solutions are obtained for different case studies. Further, the obtained solutions are verified by comparing them with the single-objective optimization solutions. Attainment surface based performance evaluation of the proposed optimization algorithm has been carried out.

scholarly journals MULTI-OBJECTIVE OPTIMIZATION ALGORITHM FOR POWER MANAGEMENT IN COGNITIVE RADIO NETWORKS

2019 ◽  
Vol 2019 (02) ◽  
pp. 97-109 ◽  
Author(s):  
Wang Haoxiang
Keyword(s):  
Cognitive Radio ◽  
Power Management ◽  
Cognitive Radio Networks ◽  
Optimization Algorithm ◽  
Cognitive Radios ◽  
Radio Networks ◽  
Optimization Techniques ◽  
Network Simulator ◽  
Multi Objective Optimization ◽  
Multi Objective

The cognitive radio networks is an adaptive and intelligent radio network that is capable of automatically identifying the available channels in the spectrum that is wireless. Cognitive radios modify the parameters supporting the conveyance according to the needs of communication to enhance the operating radio behavior and avail a concurrent communication within the allotted spectrum band at one location. To improvise the parameter configuration the intelligent optimization techniques are been followed nowadays. The paper puts forth a multi-objective optimization algorithm (MO-OPA) for the power management in the cognitive radio networks. The proposed method utilizes the hybridized evolutionary algorithm to reduce the power consumption by minimizing the delay in the communication, intervention and the error rate of the packets. The validation of the proposed method is done to using the network simulator-2 to evince the capabilities of the proposed MO-OPA.

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