Chimera: Enabling Hierarchy Based Multi-Objective Optimization for Self-Driving Laboratories

2018 ◽  
Author(s):  
Florian Häse ◽  
Loic Roch ◽  
Alan Aspuru-Guzik
Keyword(s):  
Energy Transport ◽  
Optimization Problems ◽  
Direct Injection ◽  
A Priori ◽  
Optimization Algorithms ◽  
Detailed Knowledge ◽  
Multi Objective Optimization ◽  
Practical Applications ◽  
Multi Objective ◽  
Sampling Sequence

<div><div>We introduce Chimera, a general purpose achievement scalarizing function (ASF) for multi-objective optimization problems in experiment design. Chimera combines concepts of a priori scalarizing with ideas from lexicographic approaches. It constructs a single merit-based function which implicitly accounts for a provided hierarchy in the objectives. The performance of the suggested ASF is demonstrated on several well-established analytic multi-objective benchmark sets using different single-objective optimization algorithms. We further illustrate the performance and applicability of Chimera on two practical applications: (i) the auto-calibration of a virtual robotic sampling sequence for direct-injection, and (ii) the inverse-design of a system for efficient excitation energy transport. The results indicate that Chimera enables a wide class of optimization algorithms to rapidly find solutions. The presented applications highlight the interpretability of Chimera to corroborate design choices on tailoring system parameters. Additionally, Chimera appears to be applicable to any set of n unknown objective functions, and more importantly does not require detailed knowledge about these objectives. We recommend the use of Chimera in combination with a variety of optimization algorithms for an efficient and robust optimization of multi-objective problems.</div></div><div><br></div>

Chimera: Enabling Hierarchy Based Multi-Objective Optimization for Self-Driving Laboratories

2018 ◽  
Author(s):  
Florian Häse ◽  
Loic Roch ◽  
Alan Aspuru-Guzik
Keyword(s):  
Energy Transport ◽  
Optimization Problems ◽  
Direct Injection ◽  
A Priori ◽  
Optimization Algorithms ◽  
Detailed Knowledge ◽  
Multi Objective Optimization ◽  
Practical Applications ◽  
Multi Objective ◽  
Sampling Sequence

<div><div>We introduce Chimera, a general purpose achievement scalarizing function (ASF) for multi-objective optimization problems in experiment design. Chimera combines concepts of a priori scalarizing with ideas from lexicographic approaches. It constructs a single merit-based function which implicitly accounts for a provided hierarchy in the objectives. The performance of the suggested ASF is demonstrated on several well-established analytic multi-objective benchmark sets using different single-objective optimization algorithms. We further illustrate the performance and applicability of Chimera on two practical applications: (i) the auto-calibration of a virtual robotic sampling sequence for direct-injection, and (ii) the inverse-design of a system for efficient excitation energy transport. The results indicate that Chimera enables a wide class of optimization algorithms to rapidly find solutions. The presented applications highlight the interpretability of Chimera to corroborate design choices on tailoring system parameters. Additionally, Chimera appears to be applicable to any set of n unknown objective functions, and more importantly does not require detailed knowledge about these objectives. We recommend the use of Chimera in combination with a variety of optimization algorithms for an efficient and robust optimization of multi-objective problems.</div></div><div><br></div>

A Manufacturing Oriented Single Point Search Hyper-Heuristic Scheme for Multi-Objective Optimization

2017 ◽  
Author(s):  
Pei Cao ◽  
Zhaoyan Fan ◽  
Robert Gao ◽  
Jiong Tang
Keyword(s):  
Simulated Annealing Algorithm ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Single Point ◽  
Optimization Techniques ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Practical Applications ◽  
Multi Objective ◽  
Point Search

Multi-objective optimization problems are frequently encountered in engineering analyses. Optimization techniques in practical applications are devised and evaluated mostly for specific problems, and thus may not be generally applicable when applications vary. In this study we formulate a probability matching based hyper-heuristic scheme, then propose four low-level heuristics which can work coherently with the single point search algorithm MOSA/R (Multi-Objective Simulated Annealing Algorithm based on Re-pick) towards multi-objective optimization problems of various properties, namely DTLZ and UF test instances. Making use of the domination amount, crowding distance and hypervolume calculations, the hyper-heuristic scheme could meet different optimization requirements. The approach developed (MOSA/R-HH) exhibits better and more robust performance compared to AMOSA, NSGA-II and MOEA/D as illustrated in the numerical tests. The outcome of this research may potentially benefit various design and manufacturing practices.

scholarly journals A self-adaptive population Rao algorithm for optimization of selected bio-energy systems

2020 ◽  
Author(s):  
R Venkata Rao ◽  
Hameer Singh Keesari
Keyword(s):  
Optimization Algorithm ◽  
Optimization Problems ◽  
Optimization Algorithms ◽  
Energy Systems ◽  
Design Parameters ◽  
Perturbation Equation ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Fitness Value ◽  
Self Adaptive

Abstract This work proposes a metaphor-less and algorithm-specific parameter-less algorithm, named as self-adaptive population Rao algorithm, for solving the single-, multi-, and many-objective optimization problems. The proposed algorithm adapts the population size based on the improvement in the fitness value during the search process. The population is randomly divided into four sub-population groups. For each sub-population, a unique perturbation equation is randomly allocated. Each perturbation equation guides the solutions toward different regions of the search space. The performance of the proposed algorithm is examined using standard optimization benchmark problems having different characteristics in the single- and multi-objective optimization scenarios. The results of the application of the proposed algorithm are compared with those obtained by the latest advanced optimization algorithms. It is observed that the results obtained by the proposed method are superior. Furthermore, the proposed algorithm is used to identify optimum design parameters through multi-objective optimization of a fertilizer-assisted microalgae cultivation process and many-objective optimization of a compression ignition biodiesel engine system. From the results of the computational tests, it is observed that the performance of the self-adaptive population Rao algorithm is superior or competitive to the other advanced optimization algorithms. The performances of the considered bio-energy systems are improved by the application of the proposed optimization algorithm. The proposed optimization algorithm is more robust and may be easily extended to solve single-, multi-, and many-objective optimization problems of different science and engineering disciplines.

scholarly journals Optimization Algorithms for System Integration

2008 ◽  
Vol 56 ◽  
pp. 514-523
Author(s):  
Costas Papadimitriou ◽  
Evaggelos Ntotsios
Keyword(s):  
Parameter Estimation ◽  
Damage Detection ◽  
System Analysis ◽  
Heuristic Algorithms ◽  
Model Updating ◽  
Optimization Problems ◽  
Optimization Algorithms ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Continuous And Discrete Variables

This work outlines the optimization algorithms involved in integrating system analysis and measured data collected from a network of sensors. The integration is required for structural health monitoring problems arising in structural dynamics and related to (1) model parameter estimation used for finite element model updating, (2) model-based damage detection in structures and (3) optimal sensor location for parameter estimation and damage detection. These problems are formulated as single- and multi-objective optimization problems of continuous or discrete-valued variables. Gradient-based, evolutionary, hybrid and heuristic algorithms are presented that effectively address issues related to the estimation of multiple local/global solutions and computational complexity arising in single and multi-objective optimization involving continuous and discrete variables.

scholarly journals REVIEW OF THE MULTI-OBJECTIVE SWARM INTELLIGENCE OPTIMIZATION ALGORITHMS

2021 ◽  
Vol 20 (Number 2) ◽  
pp. 171-211
Author(s):  
Shaymah Akram Yasear ◽  
Ku Ruhana Ku-Mahamud
Keyword(s):  
Swarm Intelligence ◽  
Performance Metrics ◽  
Optimization Problems ◽  
Optimization Algorithms ◽  
Gravitational Search Algorithm ◽  
Future Research ◽  
Grey Wolf Optimizer ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Advantages And Disadvantages

Multi-objective swarm intelligence (MOSI) metaheuristics were proposed to solve multi-objective optimization problems (MOPs) that consists of two or more conflict objectives, in which improving an objective leads to the degradation of the other. The MOSI algorithms are based on the integration of single objective algorithms and multi-objective optimization (MOO) approach. The MOO approaches include scalarization, Pareto dominance, decomposition and indicator-based. In this paper, the status of MOO research and state-of-the-art MOSI algorithms namely, multi-objective particle swarm, artificial bee colony, firefly algorithm, bat algorithm, gravitational search algorithm, grey wolf optimizer, bacterial foraging and moth-flame optimization algorithms have been reviewed. These reviewed algorithms were mainly developed to solve continuous MOPs. The review is based on how the algorithms deal with objective functions using MOO approaches, the benchmark MOPs used in the evaluation and performance metrics. Furthermore, it describes the advantages and disadvantages of each MOO approach and provides some possible future research directions in this area. The results show that several MOO approaches have not been used in most of the proposed MOSI algorithms. Integrating other different MOO approaches may help in developing more effective optimization algorithms, especially in solving complex MOPs. Furthermore, most of the MOSI algorithms have been evaluated using MOPs with two objectives, which clarifies open issues in this research area.

scholarly journals REVISIÓN SOBRE ALGORITMOS DE OPTIMIZACIÓN MULTI-OBJETIVO GENÉTICOS Y BASADOS EN ENJAMBRES DE PARTÍCULAS

2016 ◽  
Vol 6 (2) ◽  
pp. 54
Author(s):  
Joaquín Javier Meza Álvarez ◽  
Juan Manuel Cueva Lovelle ◽  
Helbert Eduardo Espitia
Keyword(s):  
Social Behavior ◽  
Evolutionary Computation ◽  
Optimization Algorithm ◽  
Critical Review ◽  
Optimization Problems ◽  
Optimization Algorithms ◽  
Good Alternative ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Good View

El enfoque evolutivo como también el comportamiento social han mostrado ser una muy buena alternativa en los problemas de optimización donde se presentan varios objetivos a optimizar. De la misma forma, existen todavía diferentes vias para el desarrollo de este tipo de algoritmos. Con el fin de tener un buen panorama sobre las posibles mejoras que se pueden lograr en los algoritmos de optimización bio-inspirados multi-objetivo es necesario establecer un buen referente de los diferentes enfoques y desarrollos que se han realizado hasta el momento.En este documento se revisan los algoritmos de optimización multi-objetivo más recientes tanto genéticos como basados en enjambres de partículas. Se realiza una revisión critica con el fin de establecer las características más relevantes de cada enfoque y de esta forma identificar las diferentes alternativas que se tienen para el desarrollo de un algoritmo de optimización multi-objetivo bio-inspirado.Review about genetic multi-objective optimization algorithms and based in particle swarmABSTRACTThe evolutionary approach as social behavior have proven to be a very good alternative in optimization problems where several targets have to be optimized. Likewise, there are still different ways to develop such algorithms. In order to have a good view on possible improvements that can be achieved in the optimization algorithms bio-inspired multi-objective it is necessary to establish a good reference of different approaches and developments that have taken place so far. In this paper the algorithms of multi-objective optimization newest based on both genetic and swarms of particles are reviewed. Critical review in order to establish the most relevant characteristics of each approach and thus identify the different alternatives have to develop an optimization algorithm multi-purpose bio-inspired design is performed.Keywords: evolutionary computation, evolutionary multi-objective optimization.

A Scalable D Dominant Strategy to Solve Large-Dimensional Multi-Objective Optimization Problems

2014 ◽  
Vol 1037 ◽  
pp. 383-388
Author(s):  
Qiong Yuan ◽  
Guang Ming Dai
Keyword(s):  
Evolutionary Algorithms ◽  
Optimization Problems ◽  
Optimization Algorithms ◽  
Dominant Strategy ◽  
Numerical Comparison ◽  
Multi Objective Optimization ◽  
Dominant Mechanism ◽  
Multi Objective ◽  
Comparison Test ◽  
Research Areas

Solving large-dimensional multi-objective optimization problems is one of the focus research areas of multi-objective optimization evolutionary . When using traditional multi-objective optimization algorithms to solve large-dimensional multi-objective optimization problems,we found that the unsatisfactory optimizing results often exist. To overcome this flaw, in this paper we studied scalable dominant mechanism and proposed a D dominant strategy. According to the superior theory of D strategy ,we improved the current four kinds of typical multi-objective optimization evolutionary algorithms. The numerical comparison test on DTLZ1-6 (20) questions which were solved by the improved algorithms indicated that D strategy had in varying degrees improved the algorithms for solving large-dimensional multi-objective optimization problems .Thus ,we confirmed that the D strategy for solving large-dimensional multi-objective optimization problems is effective.

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