Filters








27 Hits in 5.0 sec

A parameterless penalty rule-based fitness estimation for decomposition-based many-objective optimization evolutionary algorithm

Junhua Liu, Yuping Wang, Shiwei Wei, Xiangjuan Wu, Wuning Tong
2019 IEEE Access  
Then, based on NLAD-dominance, we design a new fitness estimation strategy which takes both convergence and diversity into account, and adaptively balances them by a parameterless penalty rule.  ...  To enhance the selection pressure, in this paper, through redefining each objective function by a non-linear transformation, we first propose a new dominance method called NLAD-dominance, in which a dynamic  ...  [45] proposed a non-dominated dynamic weight aggregation by using a genetic algorithm to find the Paretooptimal solutions which are sufficient to be used to learn the Pareto-optimal space with dimension  ... 
doi:10.1109/access.2019.2920698 fatcat:wnsduqmfcrdb3bs3dxp4vvukcu

A new nondominated sorting genetic algorithm based to the regression line For fuzzy traffic signal optimization problem

H. Asadi, R. Tavakkoli Moghaddam, N. Shahsavari Pour, E. Najafi
2017 Scientia Iranica. International Journal of Science and Technology  
, named PDNSGA (Non-dominated Sorting Genetic Algorithm based on Perpendicular Distance).  ...  KEYWORDS Tra c signal systems; Genetic algorithm; Vehicle and pedestrian delays; ANOVA. Abstract.  ...  Evolution Strategy (PAES), Non-dominated Sorting Genetic Algorithm (NSGA), fast Non-dominated Sorting Genetic Algorithm (NSGA-II), Strength Pareto Evolutionary Algorithm (SPEA), improved Strength Pareto  ... 
doi:10.24200/sci.2017.4442 fatcat:2jnaiar3qvezpglaggcr26upoi

Modified Constrained Differential Evolution for Solving Nonlinear Global Optimization Problems [chapter]

Md. Abul Kalam Azad, M. G. P. Fernandes
2013 Studies in Computational Intelligence  
The convergence behavior of the algorithm to handle discrete and integer variables is analyzed using four well-known mixed-integer engineering design problems.  ...  Some methods try to make the solution feasible by using penalty function methods, but the performance is not always satisfactory since the selection of the penalty parameters for the problem at hand is  ...  The author used a penalty function that does not require any penalty parameter. Barbosa and Lemonge [2] proposed a parameterless adaptive penalty scheme for genetic algorithm.  ... 
doi:10.1007/978-3-642-35638-4_7 fatcat:yjshmuvk5nefhjfat3tx7xiwxy

Large-Scale Truss-Sizing Optimization with Enhanced Hybrid HS Algorithm

Sadik Ozgur Degertekin, Mohammad Minooei, Lorenzo Santoro, Bartolomeo Trentadue, Luciano Lamberti
2021 Applied Sciences  
However, very few studies documented the use of metaheuristic algorithms in large-scale structural optimization.  ...  Metaheuristic algorithms currently represent the standard approach to engineering optimization.  ...  The above arguments indicate that very few studies focused on the use of metaheuristic algorithms in large-scale structural optimization problems.  ... 
doi:10.3390/app11073270 fatcat:mzrl4vqkrvdzxfsa6i6wsqzaae

A novel multi criteria decision making model for optimizing time–cost–quality trade-off problems in construction projects

Shahryar Monghasemi, Mohammad Reza Nikoo, Mohammad Ali Khaksar Fasaee, Jan Adamowski
2015 Expert systems with applications  
A benchmark case study of DTCQTP was solved using the proposed methodology, and the Pareto optimal solutions obtained were subsequently ranked using the ER approach.  ...  To identify all global Pareto optimal solutions, a multi-objective genetic algorithm (MOGA) incorporating the NSGA-II procedure was developed and tested in a highway construction project case study.  ...  In this study, one of the most popular and widely-used metaheuristic algorithms, the multi-objective genetic algorithm (MOGA), was used.  ... 
doi:10.1016/j.eswa.2014.11.032 fatcat:pnwogen5mjdxfdlfrb4aqrezyy

A chaotic-based improved many-objective Jaya algorithm for many-objective optimization problems

Sandeep U. Mane, M. R. Narsingrao
2021 International Journal of Industrial Engineering Computations  
The other component of the existing MaOJaya algorithm, such as non-dominated sorting, reference vector and tournament selection scheme of NSGA-II is preserved.  ...  This modification keeps the MaOJaya algorithm simple as well as, preserves its parameterless feature.  ...  Step 4: Evaluate the modified solution using Non-dominated sorting with a reference point vector and ranking method. Step 5: Combine the new solution with the old one.  ... 
doi:10.5267/j.ijiec.2020.10.001 fatcat:r7uoxrgygva4jkeccgba4uk55e

NON-DOMINATED RANKED GENETIC ALGORITHM FOR SOLVING CONSTRAINED MULTI-OBJECTIVE OPTIMIZATION PROBLEMS

Omar Jadaan, Lakshmi Rajamani, C Rao
unpublished
The new Parameterless Penalty and the Non-dominated Ranked Genetic Algorithm (PP-NRGA) continuously find better Pareto optimal set of solutions.  ...  In this paper, a method combining the new Non-dominated Ranked Genetic Algorithm (NRGA), with a parameterless penalty approach are exploited to devise the search to find Pareto optimal set of solutions  ...  CONCLUSIONS AND FUTURE WORK This paper has proposed a new constraint multiobjective evolutionary algorithm called Parameterless Penalty Non-dominated Ranking Genetic Algorithm (PP-NRGA).  ... 
fatcat:fcozce6fxne2xewedfposmxfay

Assessment of Voltage Imbalance Improvement and Power Loss Reduction in Residential Distribution Systems in Taiwan

Nien-Che Yang, Yan-Lin Zeng, Tsai-Hsiang Chen
2021 Mathematics  
In this study, the non-dominated sorting genetic algorithm II (NSGA-II) is used to optimize the annual phase arrangement of distribution transformers connected to primary feeders to improve three-phase  ...  The equivalent circuit models and solution algorithms for typical distribution systems in Taiwan are built using the commercial software package MATLAB.  ...  Therefore, the non-dominated sorting genetic algorithm (NSGA-II) has been widely adopted [19] [20] [21] due to its flexibility and efficiency.  ... 
doi:10.3390/math9243254 fatcat:zz2oktg7g5dbppyz34iwo6fszu

Methods that Optimize Multi-Objective Problems: A Survey and Experimental Evaluation

Kamal Taha
2020 IEEE Access  
We experimentally compared and ranked the optimization methods that fall under each objective function, the objective functions that fall under each objective category, and the objective categories used  ...  Moreover, most of these survey papers classify algorithms as independent of the specific techniques they employ.  ...  It is based on the Non-dominated Sorting Genetic Algorithm-II (ε-NSGA-2) and employs the crossover and mutation operators.  ... 
doi:10.1109/access.2020.2989219 fatcat:sxcq3y5ijvbb5euvjfqdgjbbxi

A Review of Metaheuristic Techniques for Optimal Integration of Electrical Units in Distribution Networks

Kayode E. Adetunji, Ivan Hofsajer, Adnan M. Abu-Mahfouz, Ling Cheng
2020 IEEE Access  
In smart grids development, metaheuristic algorithms are one of the optimization algorithms that have been applied extensively to mitigate the accompanying problems such as voltage instability, power loss  ...  This review shows a need for more research on developing efficient metaheuristic algorithms and the effective handling of multiple objective functions.  ...  The technique uses a domination-based approach to assign fitness through non-domination ranking and crowding distance [125] .  ... 
doi:10.1109/access.2020.3048438 fatcat:e2fyuaofarga3giriaittsjbuq

A Forest Representation for Evolutionary Algorithms Applied to Network Design [chapter]

A. C. B. Delbem, Andre de Carvalho
2003 Lecture Notes in Computer Science  
They are stochastic algorithms whose search methods model some natural phenomena; genetic inheritance and Darwinian strife for survival.  ...  Thus, how to design efficient algorithms suitable for complex cases of network optimization models by EA technique is a key issue of this research work.  ...  Non-dominated sorting genetic algorithm (nsGA: Deb, 1995): Srinivas and Deb (1995) also developed a Pareto ranking-based fitness assignment and it called Nondominated Sorting Genetic Algorithm (nsGA)  ... 
doi:10.1007/3-540-45105-6_74 fatcat:x7j3mwlqjffn3oagxwdr77xrrq

Interaction mining and skill-dependent recommendations for multi-objective team composition

Christoph Dorn, Florian Skopik, Daniel Schall, Schahram Dustdar
2011 Data & Knowledge Engineering  
We provide two heuristics based on Genetic Algorithms and Simulated Annealing for discovering efficient team configurations that yield the best trade-off between skill coverage and team connectivity.  ...  Web-based collaboration and virtual environments supported by various Web 2.0 concepts enable the application of numerous monitoring, mining and analysis tools to study human interactions and team formation  ...  The direct team distance (Eq. (3)) is thus defined as the sum of link weights between members plus a penalty distance for non-existing links.  ... 
doi:10.1016/j.datak.2011.06.004 pmid:22298939 pmcid:PMC3268649 fatcat:d4gu2dnt2vag5kwvgeuo2wcpne

Lost in optimisation of water distribution systems? A literature review of system operation

Helena Mala-Jetmarova, Nargiz Sultanova, Dragan Savic
2017 Environmental Modelling & Software  
Water quality parameters (such as chlorine) were typically modelled as non-conservative using first order decay kinetics, except for Murphy et al. (2007) and Prasad and Walters (2006) , who used water  ...  Optimisation methods used were mainly LP and mixed integer nonlinear programming (MINLP) (for example Arai et al. (2013), Biscos et al. (2003), Boccelli et al. (1998)) and metaheuristic algorithms (GA  ...  net present value NSGA = nondominated sorting genetic algorithm NSGA-II = nondominated sorting genetic algorithm II : Minimise (a) the pump operating costs (energy consumption charge), (b) penalty costs  ... 
doi:10.1016/j.envsoft.2017.02.009 fatcat:ryp5kbzafrb43lprrx5ptzzvme

A novel online supervised hyperparameter tuning procedure applied to cross-company software effort estimation

Leandro L. Minku
2019 Empirical Software Engineering  
A case study with the ISBSG Repository shows that the proposed tuning procedure in combination with a simple threshold-based clustering method is the most successful in enabling Dycom to drastically reduce  ...  Even though clustering methods could be used to automatically create CC subsets, there are no procedures for automatically tuning the number of clusters over time in online supervised scenarios.  ...  Acknowledgements The author is thankful to Siqing Hou for the implementation of the initial version of the code to integrate Dycom with WEKA's clustering algorithms, which was created during his internship  ... 
doi:10.1007/s10664-019-09686-w fatcat:tyfhg6nirfgrxjxflx7y5z773u

Adaptive repair method for constraint handling in multi-objective optimization based on constraint-variable relation

Faezeh Samanipour
2020
The algorithm is tested on an optimization benchmark test case and an engineering optimization problem involving the structural design of a product tanker.  ...  Also, a competitive evolutionary algorithm, MOEA/D, is used for validation of the results.  ...  Conclusion Contributions and significance In this thesis, a novel constraint handling method was developed for the non-dominance based genetic Future work The case studies in this thesis were problems  ... 
doi:10.14288/1.0388793 fatcat:n2qahbwgcfba7ikwhjndaqlp7a
« Previous Showing results 1 — 15 out of 27 results