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Multiobjective Optimization for the Stochastic Physical Search Problem [chapter]

Jeffrey Hudack, Nathaniel Gemelli, Daniel Brown, Steven Loscalzo, Jae C. Oh
2015 Lecture Notes in Computer Science  
We model an intelligence collection activity as multiobjective optimization on a binary stochastic physical search problem, providing formal definitions of the problem space and nondominated solution sets  ...  We present the Iterative Domination Solver as an approximate method for generating solution sets that can be used by a human decision maker to meet the goals of a mission.  ...  Problem formulation We formulate the AS-ISR problem as a stochastic physical search problem (SPSP), where we are given an undirected network G(S + , E) with a set of sites S + = S ∪ {o, d} where S = {s  ... 
doi:10.1007/978-3-319-19066-2_21 fatcat:sbbnvkjnfzfuxpkrw2vypmauci

Efficient Hybrid Multiobjective Optimization of Pressure Swing Adsorption

Zhimian Hao, Adrian Caspari, Artur M. Schweidtmann, Yannic Vaupel, Alexei A. Lapkin, Adel Mhamdi
2021 Chemical Engineering Journal  
In the first step, a Bayesian stochastic multiobjective optimization algorithm (i.e., TSEMO) searches the entire decision space and identifies an approximated Pareto front within a small number of simulations  ...  A B S T R A C T Pressure swing adsorption (PSA) is an energy-efficient technology for gas separation, while the multiobjective optimization of PSA is a challenging task.  ...  Tobias Ploch (RWTH Aachen) for helping to debug the PSA model in Dymola.  ... 
doi:10.1016/j.cej.2021.130248 fatcat:vt5shizmaneklgerpzvmd374lu

Stochastic Fractal Based Multiobjective Fruit Fly Optimization

Cili Zuo, Lianghong Wu, Zhao-Fu Zeng, Hua-Liang Wei
2017 International Journal of Applied Mathematics and Computer Science  
In this study, a novel stochastic fractal model based fruit fly optimization algorithm is proposed for multiobjective optimization.  ...  To deal with multiobjective optimization problems, the Pareto domination concept is integrated into the selection process of fruit fly optimization and a novel multiobjective fruit fly optimization algorithm  ...  Acknowledgment This work was partly supported by the National Natural Science  ... 
doi:10.1515/amcs-2017-0029 fatcat:huzywkjtxzf3veylfs4prx7fyq

A new competitive multiverse optimization technique for solving single‐objective and multiobjective problems

Ilyas Benmessahel, Kun Xie, Mouna Chellal
2020 Engineering Reports  
The development of useful algorithms for solving global optimization problems has recently drawing the research community's attention.  ...  In this work, a novel population-based optimization technique is proposed, the so-called competitive multiverse optimizer (CMVO) for solving global optimization problems.  ...  approaches that are not stochastic (ie, dependent on the problem), metaheuristics optimize problems gradually and stochastically find the optimal solution (ie, independent on the problem).  ... 
doi:10.1002/eng2.12124 fatcat:eg6hepznvjcdzcg5raj7facmqe

Dingo Optimizer: A Nature-Inspired Metaheuristic Approach for Engineering Problems

Amit Kumar Bairwa, Sandeep Joshi, Dilbag Singh, Yann Favennec
2021 Mathematical Problems in Engineering  
Optimization is a buzzword, whenever researchers think of engineering problems.  ...  Some of the well-known test functions are used for the comparative study of this work. The results reveal that the dingo optimizer performed significantly better than other nature-inspired algorithms.  ...  (HS) [37] Multiobjective HS [38] Tabu search (TS) [39] Multiobjective TS [40] Parameter adaptive harmony search (PAHS) [41] Multiobjective PAHS [41] Group search optimizer (GSO) [42] Multiobjective  ... 
doi:10.1155/2021/2571863 fatcat:gvws4rhidneljoasxcm5372qky

Multiobjective evolutionary algorithms: A survey of the state of the art

Aimin Zhou, Bo-Yang Qu, Hui Li, Shi-Zheng Zhao, Ponnuthurai Nagaratnam Suganthan, Qingfu Zhang
2011 Swarm and Evolutionary Computation  
for multimodal problems, constraint handling and MOEAs, computationally expensive multiobjective optimization problems (MOPs), dynamic MOPs, noisy MOPs, combinatorial and discrete MOPs, benchmark problems  ...  A multiobjective optimization problem involves several conflicting objectives and has a set of Pareto optimal solutions.  ...  Jaszkiewicz [79] proposed a multiobjective genetic local search (MOGLS) algorithm for the multiobjective 0/1 knapsack problem.  ... 
doi:10.1016/j.swevo.2011.03.001 fatcat:jfcghitjp5ap5he4d3ackhhjsu

Optimal Power Flow Techniques under Characterization of Conventional and Renewable Energy Sources: A Comprehensive Analysis

Baseem Khan, Pawan Singh
2017 Journal of Engineering  
The exhaustive knowledge of optimal power flow (OPF) methods is critical for proper system operation and planning, since OPF methods are utilized for finding the optimal state of any system under system  ...  Incorporating renewable energy sources optimized the power flow of system under different constraints.  ...  Conflicts of Interest The authors declare that there are no conflicts of interest regarding the publication of this paper.  ... 
doi:10.1155/2017/9539506 fatcat:ybh7nsn7ybdkng6756pjxey5je

IS-PAES: A Constraint-Handling Technique Based on Multiobjective Optimization Concepts [chapter]

Arturo Hernández Aguirre, Salvador Botello Rionda, Giovanni Lizárraga Lizárraga, Carlos A. Coello Coello
2003 Lecture Notes in Computer Science  
shrinking the constrained space of single-objective optimization problems.  ...  This paper introduces a new constraint-handling method called Inverted-Shrinkable PAES (IS-PAES), which focuses the search effort of an evolutionary algorithm on specific areas of the feasible region by  ...  The first author acknowledges support from CONACyT project I-39324-A. The second author acknowledges support from CONACyT project No. 34575-A.  ... 
doi:10.1007/3-540-36970-8_6 fatcat:sqfiynbazbftxef4zkntj55osq

A hierarchical evolutionary algorithm for multiobjective optimization in IMRT

Clay Holdsworth, Minsun Kim, Jay Liao, Mark H. Phillips
2010 Medical Physics (Lancaster)  
The MOEA incorporates clinical criteria to restrict the search space through protocol objectives and then uses Pareto optimality among the fitness objectives to select individuals.  ...  Results: Acceleration techniques implemented on both levels of the hierarchical algorithm resulted in short, practical runtimes for optimizations.  ...  A multiobjective optimization ͑MOO͒ algorithm is one method for searching through the space of feasible plans.  ... 
doi:10.1118/1.3478276 pmid:20964218 pmcid:PMC2945740 fatcat:pksdvtiyf5a43g3ijgkvsczo6q

Antenna Optimization Using Multiobjective Algorithms

X. L. Travassos, D. A. G. Vieira, A. C. Lisboa
2012 ISRN Communications and Networking  
This paper presents several applications of multiobjective optimization to antenna design, emphasizing the main general steps in this process.  ...  Both stochastic and deterministic methods are considered in the analysis.  ...  However, most of the problems can be divided into search for optimal solutions and approximate solution of Maxwell's equations using numerical methods.  ... 
doi:10.5402/2012/369293 fatcat:rmn74urlfjdjvp4u5mcajnkg6q

Beyond the Pareto Efficient Frontier: Constraint Active Search for Multiobjective Experimental Design

Gustavo Malkomes, Bolong Cheng, Eric Hans Lee, Mike Mccourt
2021 International Conference on Machine Learning  
We propose a new formulation that accounts for the importance of the parameter space and is thus more suitable for multiobjective design problems; instead of searching for the Paretoefficient frontier,  ...  Sample-efficient multiobjective optimization methods focus on the objective function values in metric space and ignore the sampling behavior of the design configurations in parameter space.  ...  Acknowledgements We want to thank Sajad Haghanifar and Paul Leu for providing the scanning electron microscope images and motivating the research presented in this paper.  ... 
dblp:conf/icml/MalkomesCLM21 fatcat:5zpy2d4qu5eexn56wun7pflxni

Multiobjective Optimization Method Based on Adaptive Parameter Harmony Search Algorithm

P. Sabarinath, M. R. Thansekhar, R. Saravanan
2015 Journal of Applied Mathematics  
In this paper, we extend the recently developed parameter adaptive harmony search algorithm to solve multiobjective design optimization problems using the weighted sum approach.  ...  Solving a nonlinear multiobjective optimization problem requires significant computing effort.  ...  The multiobjective disc brake optimization problem was solved by Osyczka and Kundu [21] using plain stochastic method and genetic algorithms for optimization of disc brake problem.  ... 
doi:10.1155/2015/165601 fatcat:ey22323t65drplxapshdy6l2uq

Multivariate Stochastic Optimization Approach Applied in a Flux-Cored Arc Welding Process

Alexandre F. Torres, Franco B. Rocha, Fabrmcio A. Almeida, Jose H. F. Gomes, Anderson P. Paiva, Pedro P. Balestrassi
2020 IEEE Access  
The weighting-sums method was applied to formulate the multiobjective optimization problem. It was possible to formulate a multivariate probability distribution for the penetration and dilution.  ...  However, several optimization problems that use stochastic programming do not consider the impact of the correlation between the output variables on their probabilistic constraints.  ...  for the multiobjective optimization problem.  ... 
doi:10.1109/access.2020.2983566 fatcat:fggobmmen5bkrfixffsdef3giy

The Application of Multiobjective Genetic Algorithm to the Parameter Optimization of Single-Well Potential Stochastic Resonance Algorithm Aimed at Simultaneous Determination of Multiple Weak Chromatographic Peaks

Haishan Deng, Shaofei Xie, Bingren Xiang, Ying Zhan, Wei Li, Xiaohua Li, Caiyun Jiang, Xiaohong Wu, Dan Liu
2014 The Scientific World Journal  
Therefore, multiobjective genetic algorithm was employed to optimize the parameter of SSRA for multiple optimization objectives (i.e.,S/Nand peak shape) and multiple chromatographic peaks.  ...  However, the optimization of the parameters is complicated and time consuming, although the single-well potential stochastic resonance algorithm (SSRA) has already reduced the number of parameters to only  ...  GAs can search for many noninferior solutions in parallel by maintaining a population of solutions. Therefore, GAs are very suitable for solving the problems of multiobjective optimization [23] .  ... 
doi:10.1155/2014/767018 pmid:24526920 pmcid:PMC3913510 fatcat:n5zxtnxok5hvrpeztengpex26q

Advances and challenges of quantitative verification and synthesis for cyber-physical systems

Marta Kwiatkowska
2016 2016 Science of Security for Cyber-Physical Systems Workshop (SOSCYPS)  
to support the design process, and hence improve the reliability and reduce production costs.  ...  This paper gives an overview of quantitative verification and synthesis techniques developed for cyber-physical systems, summarising recent achievements and future challenges in this important field.  ...  The parameter synthesis problem aims to find an optimal value of the parameter that guarantees the satisfaction of a quantitative temporal logic property.  ... 
doi:10.1109/soscyps.2016.7579999 dblp:conf/cpsweek/Kwiatkowska16 fatcat:ugbkizf4xrcgzjvgtutecnepuu
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