Large permeability metamaterial design optimization for low frequency applications

2020 ◽  
Vol 64 (1-4) ◽  
pp. 369-375
Author(s):  
Yiying Li ◽  
Shiyou Yang
Keyword(s):  
Genetic Algorithm ◽  
Optimization Problems ◽  
Low Frequency ◽  
Primary Objective ◽  
Test Function ◽  
Multi Objective ◽  
Optimal Direction ◽  
Cellular Genetic Algorithm ◽  
Secondary Objective ◽  
Novel Method

This paper presents a novel method for solving multi-objective optimization problems based on single-objective cellular genetic algorithm. In the proposed multi-objective cellular genetic algorithm, the objectives are divided into the primary objective and the secondary objective according to the preferences of a decision maker. The primary objective is used as the driving force for individual updating, while the secondary objective is employed as the bias force to select neighbors. The proposed approach has ensured that the secondary objective is also evolving in the optimal direction, as evidenced by the numerical results on both a mathematical test function and a prototype metamaterial unit as reported in this paper.

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 A Multi-Objective Approach to Mitigate Negative Side Effects

2020 ◽  
Author(s):  
Sandhya Saisubramanian ◽  
Ece Kamar ◽  
Shlomo Zilberstein
Keyword(s):  
Side Effects ◽  
Empirical Evaluation ◽  
Primary Objective ◽  
Maximum Deviation ◽  
Negative Side ◽  
Multi Objective ◽  
Markov Decision ◽  
Secondary Objective ◽  
Negative Side Effects ◽  
The Impact

Agents operating in unstructured environments often create negative side effects (NSE) that may not be easy to identify at design time. We examine how various forms of human feedback or autonomous exploration can be used to learn a penalty function associated with NSE during system deployment. We formulate the problem of mitigating the impact of NSE as a multi-objective Markov decision process with lexicographic reward preferences and slack. The slack denotes the maximum deviation from an optimal policy with respect to the agent's primary objective allowed in order to mitigate NSE as a secondary objective. Empirical evaluation of our approach shows that the proposed framework can successfully mitigate NSE and that different feedback mechanisms introduce different biases, which influence the identification of NSE.

Multi-Objective Evolutionary Algorithm Based on Two Reference Points Decomposition and Historical Information Prediction

2021 ◽  
Vol 01 ◽  
Author(s):  
Er-chao Li ◽  
Kang-wei Li
Keyword(s):  
Evolutionary Algorithm ◽  
Optimization Problems ◽  
Reference Points ◽  
Multi Objective Optimization ◽  
Test Function ◽  
Historical Information ◽  
Multi Objective ◽  
Nadir Point ◽  
Overall Performance ◽  
Better Than

Aims: The main purpose of this paper is to solve the issues that the poor quality of offspring solutions generated by traditional evolutionary operators, and that the inability of the evolutionary algorithm based on decomposition to better solve the multi-objective optimization problems (MOPs) with complicated Pareto fronts (PFs). Background: For some complicated multi-objective optimization problems, the effect of the multi-objective evolutionary algorithm based on decomposition (MOEA/D) is poor. For specific complicated problems, there is less research on improving the algorithm's performance by setting and adjusting the direction vector in the decomposition-based evolutionary algorithm. And considering that in the existing algorithms, the optimal solutions are selected according to the selection strategy in the selection stage, without considering if it could produce the better solutions in the stage of individual generation to achieve the optimization effect faster. As a result of these, a multi-objective evolutionary algorithm that is based on two reference points decomposition and historical information prediction is proposed. Objective: In order to verify the feasibility of the proposed strategy, the F-series test function with complicated PFs is used as the test function to simulate the proposed strategy. Method: Firstly, the evolutionary operator based on Historical Information Prediction (EHIP) is used to generate better offspring solutions to improve the convergence of the algorithm; secondly, the decomposition strategy based on ideal point and nadir point is used to select solutions to solve the MOPs with complicated PFs, and the decomposition method with augmentation term is used to improve the population diversity when selecting solutions according to the nadir point. Finally, the proposed algorithm is compared to several popular algorithms by the F-series test function, and the comparison is made according to the corresponding performance metrics. Result: The performance of the algorithm is improved obviously compared with the popular algorithms after using the EHIP. When the decomposition method with augmentation term is added, the performance of the proposed algorithm is better than the algorithm with only the EHIP on the whole. However, the overall performance is better than the popular algorithms. Conclusion and Prospect: The experimental results show that the overall performance of the proposed algorithm is superior to the popular algorithms. The EHIP can produce better quality offspring solutions, and the decomposition strategy based on two reference points can well solve the MOPs with complicated PFs. This paper mainly demonstrates the theory without testing the practical problems. The following research mainly focuses on the application of the proposed algorithm to the practical problems such as robot path planning.

scholarly journals A Methodology for the Hybridization Based in Active Components: The Case of cGA and Scatter Search

2016 ◽  
Vol 2016 ◽  
pp. 1-11
Author(s):  
Andrea Villagra ◽  
Enrique Alba ◽  
Guillermo Leguizamón
Keyword(s):  
Genetic Algorithm ◽  
Ad Hoc ◽  
Optimization Problems ◽  
Scatter Search ◽  
Hybrid Approach ◽  
Active Components ◽  
New Techniques ◽  
The Past ◽  
Cellular Genetic Algorithm ◽  
Optimization Search

This work presents the results of a new methodology for hybridizing metaheuristics. By first locating the active components (parts) of one algorithm and then inserting them into second one, we can build efficient and accurate optimization, search, and learning algorithms. This gives a concrete way of constructing new techniques that contrasts the spread ad hoc way of hybridizing. In this paper, the enhanced algorithm is a Cellular Genetic Algorithm (cGA) which has been successfully used in the past to find solutions to such hard optimization problems. In order to extend and corroborate the use of active components as an emerging hybridization methodology, we propose here the use of active components taken from Scatter Search (SS) to improve cGA. The results obtained over a varied set of benchmarks are highly satisfactory in efficacy and efficiency when compared with a standard cGA. Moreover, the proposed hybrid approach (i.e., cGA+SS) has shown encouraging results with regard to earlier applications of our methodology.

An Improved Constrained Optimization Multi-Objective Genetic Algorithm and its Application in Engineering

2014 ◽  
Vol 962-965 ◽  
pp. 2903-2908
Author(s):  
Yun Lian Liu ◽  
Wen Li ◽  
Tie Bin Wu ◽  
Yun Cheng ◽  
Tao Yun Zhou ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Constrained Optimization ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Crossover Operator ◽  
Multi Objective Optimization ◽  
Constrained Optimization Problem ◽  
Great Ability ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm

An improved multi-objective genetic algorithm is proposed to solve constrained optimization problems. The constrained optimization problem is converted into a multi-objective optimization problem. In the evolution process, our algorithm is based on multi-objective technique, where the population is divided into dominated and non-dominated subpopulation. Arithmetic crossover operator is utilized for the randomly selected individuals from dominated and non-dominated subpopulation, respectively. The crossover operator can lead gradually the individuals to the extreme point and improve the local searching ability. Diversity mutation operator is introduced for non-dominated subpopulation. Through testing the performance of the proposed algorithm on 3 benchmark functions and 1 engineering optimization problems, and comparing with other meta-heuristics, the result of simulation shows that the proposed algorithm has great ability of global search. Keywords: multi-objective optimization;genetic algorithm;constrained optimization problem;engineering application

Cultural-Based Multi-Objective Particle Swarm Optimization for EEG Channel Reduction in Multi-Class Brain-Computer Interfaces

2012 ◽  
Vol 239-240 ◽  
pp. 1027-1032 ◽  
Author(s):  
Qing Guo Wei ◽  
Yan Mei Wang ◽  
Zong Wu Lu
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Problems ◽  
Real Life ◽  
Particle Swarm ◽  
Channel Selection ◽  
L1 Norm ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Channel Reduction ◽  
Novel Method

Applying many electrodes is undesirable for real-life brain-computer interface (BCI) application since the recording preparation can be troublesome and time-consuming. Multi-objective particle swarm optimization (MOPSO) has been widely utilized to solve multi-objective optimization problems and thus can be employed for channel selection. This paper presented a novel method named cultural-based MOPSO (CMOPSO) for channel selection in motor imagery based BCI. The CMOPSO method introduces a cultural framework to adapt the personalized flight parameters of the mutated particles. A comparison between the proposed algorithm and typical L1-norm algorithm was conducted, and the results showed that the proposed approach is more effective in selecting a smaller subset of channels while maintaining the classification accuracy unreduced.

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