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GAMBIT: A Parameterless Model-Based Evolutionary Algorithm for Mixed-Integer Problems

Krzysztof L. Sadowski, Dirk Thierens, Peter A.N. Bosman
2018 Evolutionary Computation  
We revisit a recently introduced model-based evolutionary algorithm for the MI domain, the Genetic Algorithm for Model-Based mixed-Integer opTimization (GAMBIT).  ...  Population size, for example, is an essential parameter in evolutionary computation.  ...  Instead, it treats the constrained space as a black-box and utilizes the penalty function method and the clustering mechanism to solve constrained problems.  ... 
doi:10.1162/evco_a_00206 pmid:28207296 fatcat:hhymbqdj3ze6dfiz5d557rj4yu


Youssef ALIOUI, Reşat ACAR
2020 Journal of Scientific Perspectives  
Over the last decades, evolutionary algorithms have been largely used in solving optimization problems in various fields of science.  ...  The aim of this study is to evaluate the performance of a constrained version of the Nondominated Sorting Genetic Algorithm 2 (NSGA 2), a multi-objective evolutionary optimization algorithm, written in  ...  To handle the constraints a penalty function based technique is widely used, it converts the problem to an unconstrained optimization problem by adding a penalty to the objective function value for every  ... 
doi:10.26900/jsp.4.011 fatcat:pstxblhv3vagdchg375k5wssra

Enhancement of combined heat and power economic dispatch using self adaptive real-coded genetic algorithm

P. Subbaraj, R. Rengaraj, S. Salivahanan
2009 Applied Energy  
The SARGA integrates penalty parameterless constraint handling strategy and simultaneously handles equality and inequality constraints.  ...  The SARGA is applied to solve CHPED problem with bounded feasible operating region which has large number of local minima.  ...  In penalty parameterless approach, there is no need to have any penalty parameter: the objective function value is not computed for any infeasible solution.  ... 
doi:10.1016/j.apenergy.2008.10.002 fatcat:enu5bbgqvncsvfnm7dlqab6mda

The Performance of Penalty Methods on Tree-Seed Algorithm for Numerical Constrained Optimization Problems

Ahmet Cinar, Mustafa Kiran
2020 ˜The œinternational Arab journal of information technology  
The constraint handling methods are integrated into these algorithms for solving constrained optimization problems.  ...  The constraints are the most important part of many optimization problems. The metaheuristic algorithms are designed for solving continuous unconstrained optimization problems initially.  ...  Acknowledgments The first author wish to thank Scientific Research Projects Coordinatorship at Selcuk University and The Scientific and Technological Research Council of Turkey for their institutional  ... 
doi:10.34028/iajit/17/5/13 fatcat:na6v7lkserfvxnjubhibwktwia

Reentry Trajectory Optimization Based On Differential Evolution

Songtao Chang, Yongji Wang, Lei Liu, Dangjun Zhao
2011 Zenodo  
Reentry trajectory optimization is a multi-constraints optimal control problem which is hard to solve.  ...  To handle constraints, we proposed a parameterless constraints handle process. Through comprehensive analyze the problem, we use a new algorithm integrated by DE and Newton-Raphson to solve it.  ...  Constraints handling Many researchers added a penalty function to the objective function to deal with constrained optimization. But it is difficult to choose adequate coefficient of each constraint.  ... 
doi:10.5281/zenodo.1058879 fatcat:bhpu5lzoqzhwnhqfqd4jaophg4

Looking Inside Particle Swarm Optimization in Constrained Search Spaces [chapter]

Jorge Isacc Flores-Mendoza, Efrén Mezura-Montes
2008 Lecture Notes in Computer Science  
In this paper, the behavior of different Particle Swarm Optimization (PSO) variants is analyzed when solving a set of well-known numerical constrained optimization problems.  ...  Some conclusions regarding the behavior of PSO in constrained search spaces and the improved results presented by the modified PSO are given and the future work is established.  ...  Krohling and Do Santos Coehlo [8] used a co-evolutionary approach in a Lagrangian function with two sub-swarms, one of them optimizes the original problem and the other one aims to find the optimal values  ... 
doi:10.1007/978-3-540-88636-5_43 fatcat:i3mec2ix7vb33at2l2zwb5kpda

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.  ...  As a result, NLAD-dominance can provide proper selection pressure for different kinds of MaOPs in different stages of evolution.  ...  into account, and adaptively balances them by the proposed parameterless penalty rule.  ... 
doi:10.1109/access.2019.2920698 fatcat:wnsduqmfcrdb3bs3dxp4vvukcu

Redefining the application of an evolutionary algorithm for the optimal pipe sizing problem

Nikita Palod, Vishnu Prasad, Ruchi Khare
2021 Journal of Water and Climate Change  
compared with the largest number of minimum function evaluations for other evolutionary techniques.  ...  Extensive work has been reported for the optimization of water distribution networks (WDNs) using different optimization techniques.  ...  Penalty functions chosen for the problem are to be properly tuned for any constrained optimization problems.  ... 
doi:10.2166/wcc.2021.288 fatcat:34blcnsdijhmbf7gdvh2lw5wle

Constraint-handling in nature-inspired numerical optimization: Past, present and future

Efrén Mezura-Montes, Carlos A. Coello Coello
2011 Swarm and Evolutionary Computation  
In their original versions, nature-inspired search algorithms lack a mechanism to deal with the constraints of a numerical optimization problem.  ...  For each one of them, the most popular approaches are analyzed in more detail and some representative instantiations are further discussed.  ...  As expected, the static penalty function required specific values for each test problem solved.  ... 
doi:10.1016/j.swevo.2011.10.001 fatcat:uymwk3vfqjed5plfrdmou5zlv4

A Fast and Elitist Multiobjective Genetic Algorithm: NSGA-II

Kalyanmoy Deb
2002 Zenodo  
Moreover, we modify the definition of dominance in order to solve constrained multiobjective problems efficiently.  ...  Abstract—Multiobjective evolutionary algorithms (EAs) that use nondominated sorting and sharing have been criticized mainly for their: 1) ( 3) computational complexity (where is the number of objectives  ...  CONSTRAINT HANDLING In the past, the first author and his students implemented a penalty-parameterless constraint-handling approach for singleobjective optimization.  ... 
doi:10.5281/zenodo.6487417 fatcat:dszgllgsb5cu7gzb7njhxpvlie

Learning and exploiting mixed variable dependencies with a model-based EA

Krzysztof L. Sadowski, Peter A.N. Bosman, Dirk Thierens
2016 2016 IEEE Congress on Evolutionary Computation (CEC)  
For such cases, model-based Evolutionary Algorithms (EAs) have been very successful in the fields of discrete and continuous optimization.  ...  Index Terms-Mixed-Integer Optimization, Modelbuilding, Evolutionary Algorithms.  ...  Model-based Evolutionary Algorithms (EAs) are a family of optimization algorithms known for their robustness and effectiveness in solving BBO problems.  ... 
doi:10.1109/cec.2016.7744347 dblp:conf/cec/SadowskiBT16 fatcat:buafe3nkqjdkldqkygnbezqdc4

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

Md. Abul Kalam Azad, M. G. P. Fernandes
2013 Studies in Computational Intelligence  
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  ...  It is shown that our method is rather effective when solving nonlinear optimization problems.  ...  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

An Improved Diversity Mechanism for Solving Constrained Optimization Problems Using a Multimembered Evolution Strategy [chapter]

Efrén Mezura-Montes, Carlos A. Coello Coello
2004 Lecture Notes in Computer Science  
The results obtained are very competitive when comparing the proposed approach against the previous version and other approaches representative of the state-of-the-art in constrained evolutionary optimization  ...  This paper presents an improved version of a simple evolution strategy (SES) to solve global nonlinear optimization problems.  ...  Introduction Evolutionary algorithms (EAs) have been successfully used to solve different types of optimization problems [1] .  ... 
doi:10.1007/978-3-540-24854-5_72 fatcat:otrup7tb7fga3e7pxogkmykyxm

Performance of a Constrained Version of MOEA/D on CTP-series Test Instances

Muhammad Asif, Rashida Adeeb, Nasser Mansoor, Wali Khan
2016 International Journal of Advanced Computer Science and Applications  
This paper embeds the threshold based penalty function in the update and replacement scheme of multi-objective evolutionary algorithm based on decomposition (MOEA/D) to find tradeoff solutions for constrained  ...  Empirical results demonstrate the effectiveness of the proposed penalty function in the MOEA/D framework for CMOPs. 498 | P a g e Fig. 1: Plots of the nondominated front with the  ...  Two penalty parameterless constraint handling techniques are employed in this framework to solve CTP-series [4] , [15] and CF-series [16] test instances.  ... 
doi:10.14569/ijacsa.2016.070665 fatcat:vbcko5vfhvdhdinyn6f5kxtxva

Threshold Based Penalty Functions for Constrained Multiobjective Optimization

Muhammad Asif, Nasser Mansoor, Rashida Adeeb, Wali Khan
2016 International Journal of Advanced Computer Science and Applications  
These penalty functions are incorporated in the update and replacement scheme of the multiobjective evolutionary algorithm based on decomposition (MOEA/D) framework to solve constrained multiobjective  ...  Empirical results show the pitfalls of the proposed penalty functions.  ...  A common way to deal with constraints in constrained optimization is to use penalty functions. In a penalty function approach, the penalty coefficients balance the objective and penalty functions.  ... 
doi:10.14569/ijacsa.2016.070282 fatcat:ljunzb7cnfa3doikvicekjwyum
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