Exploring the Feasible Space Using Constraint Consensus in Solving Constrained Optimization Problems

2016 ◽  
pp. 89-100 ◽  
Author(s):  
Noha M. Hamza ◽  
Daryl L. Essam ◽  
Ruhul A. Sarker
Keyword(s):  
Constrained Optimization ◽  
Optimization Problems ◽  
Constrained Optimization Problems ◽  
Feasible Space ◽  
Constraint Consensus

scholarly journals A constraint consensus memetic algorithm for solving constrained optimization problems

2013 ◽  
Vol 46 (11) ◽  
pp. 1447-1464 ◽  
Author(s):  
Noha M. Hamza ◽  
Ruhul A. Sarker ◽  
Daryl L. Essam ◽  
Kalyanmoy Deb ◽  
Saber M. Elsayed
Keyword(s):  
Constrained Optimization ◽  
Memetic Algorithm ◽  
Optimization Problems ◽  
Constrained Optimization Problems ◽  
Constraint Consensus

scholarly journals Constraint Consensus Based Artificial Bee Colony Algorithm for Constrained Optimization Problems

2019 ◽  
Vol 2019 ◽  
pp. 1-24 ◽  
Author(s):  
Liling Sun ◽  
Yuhan Wu ◽  
Xiaodan Liang ◽  
Maowei He ◽  
Hanning Chen
Keyword(s):  
Constrained Optimization ◽  
Artificial Bee Colony ◽  
Optimization Problems ◽  
Benchmark Problems ◽  
Constrained Optimization Problems ◽  
Constrained Problems ◽  
Bee Colony ◽  
Hybrid Heuristic Algorithm ◽  
Complex Optimization ◽  
Constraint Consensus

Over the last few decades, evolutionary algorithms (EAs) have been widely adopted to solve complex optimization problems. However, EAs are powerless to challenge the constrained optimization problems (COPs) because they do not directly act to reduce constraint violations of constrained problems. In this paper, the robustly global optimization advantage of artificial bee colony (ABC) algorithm and the stably minor calculation characteristic of constraint consensus (CC) strategy for COPs are integrated into a novel hybrid heuristic algorithm, named ABCCC. CC strategy is fairly effective to rapidly reduce the constraint violations during the evolutionary search process. The performance of the proposed ABCCC is verified by a set of constrained benchmark problems comparing with two state-of-the-art CC-based EAs, including particle swarm optimization based on CC (PSOCC) and differential evolution based on CC (DECC). Experimental results demonstrate the promising performance of the proposed algorithm, in terms of both optimization quality and convergence speed.

scholarly journals A Globally Convergent Parallel SSLE Algorithm for Inequality Constrained Optimization

2014 ◽  
Vol 2014 ◽  
pp. 1-6
Author(s):  
Zhijun Luo ◽  
Lirong Wang
Keyword(s):  
Constrained Optimization ◽  
Optimization Problems ◽  
Linear Equations ◽  
Coefficient Matrix ◽  
Descent Direction ◽  
Constrained Optimization Problems ◽  
Inequality Constrained Optimization ◽  
Globally Convergent ◽  
Variable Distribution ◽  
Systems Of Linear Equations

A new parallel variable distribution algorithm based on interior point SSLE algorithm is proposed for solving inequality constrained optimization problems under the condition that the constraints are block-separable by the technology of sequential system of linear equation. Each iteration of this algorithm only needs to solve three systems of linear equations with the same coefficient matrix to obtain the descent direction. Furthermore, under certain conditions, the global convergence is achieved.

scholarly journals An Augmented Lagrangian Method for Cardinality-Constrained Optimization Problems

2021 ◽  
Author(s):  
Christian Kanzow ◽  
Andreas B. Raharja ◽  
Alexandra Schwartz
Keyword(s):  
Constrained Optimization ◽  
Numerical Experiments ◽  
Penalty Method ◽  
Augmented Lagrangian ◽  
Constraint Qualification ◽  
Optimization Problems ◽  
Augmented Lagrangian Method ◽  
Constrained Optimization Problems ◽  
Strongly Stationary ◽  
Type Constraint

AbstractA reformulation of cardinality-constrained optimization problems into continuous nonlinear optimization problems with an orthogonality-type constraint has gained some popularity during the last few years. Due to the special structure of the constraints, the reformulation violates many standard assumptions and therefore is often solved using specialized algorithms. In contrast to this, we investigate the viability of using a standard safeguarded multiplier penalty method without any problem-tailored modifications to solve the reformulated problem. We prove global convergence towards an (essentially strongly) stationary point under a suitable problem-tailored quasinormality constraint qualification. Numerical experiments illustrating the performance of the method in comparison to regularization-based approaches are provided.

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