A graphical method for a class of Branin trajectories
Journal of Optimization Theory and Applications
Internal modeling of objective functions for global optimization
Journal of Optimization Theory and Applications
A class of filled functions for finding global minimizers of several variables
Journal of Optimization Theory and Applications
Constrained global optimization: algorithms and applications
Constrained global optimization: algorithms and applications
ACM Transactions on Mathematical Software (TOMS)
A multi-start global minimization algorithm with dynamic search trajectories
Journal of Optimization Theory and Applications
Sequential stopping rules for the multistart algorithm in global optimisation
Mathematical Programming: Series A and B
Bayesian stopping rules for multistart global optimization methods
Mathematical Programming: Series A and B
Efficient search techniques—an empirical study of the N-Queens problem
IBM Journal of Research and Development
Simulated annealing: theory and applications
Simulated annealing: theory and applications
Simulated annealing and Boltzmann machines: a stochastic approach to combinatorial optimization and neural computing
Pure adaptive search in Monte Carlo optimization
Mathematical Programming: Series A and B
Random tunneling by means of acceptance-rejection sampling for global optimization
Journal of Optimization Theory and Applications
On the convergence of the Baba and Dorea random optimization methods
Journal of Optimization Theory and Applications
An extended continuous Newton method
Journal of Optimization Theory and Applications
A collection of test problems for constrained global optimization algorithms
A collection of test problems for constrained global optimization algorithms
A trajectory-following method for unconstrained optimization
Journal of Optimization Theory and Applications
A polynomial time algorithm for the N-Queens problem
ACM SIGART Bulletin
3,000,000 Queens in less than one minute
ACM SIGART Bulletin
Parallel algorithms and architectures for very fast AI search
Parallel algorithms and architectures for very fast AI search
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Efficient local search for very large-scale satisfiability problems
ACM SIGART Bulletin
Recent advances in global optimization
Recent advances in global optimization
Numerical methods for global optimization
Recent advances in global optimization
Rigorous methods for global optimization
Recent advances in global optimization
Topographical global optimization
Recent advances in global optimization
A Novel Discrete Relaxation Architecture
IEEE Transactions on Pattern Analysis and Machine Intelligence
Minimizing conflicts: a heuristic repair method for constraint satisfaction and scheduling problems
Artificial Intelligence - Special volume on constraint-based reasoning
Pure adaptive search in global optimization
Mathematical Programming: Series A and B
Trajectory-following algorithms for min-max optimization problems
Journal of Optimization Theory and Applications
Importance of search-domain reduction in random optimization
Journal of Optimization Theory and Applications
A continuous approach to inductive inference
Mathematical Programming: Series A and B
Terminal Repeller Unconstrained Subenergy Tunneling (TRUST) for fast global optimization
Journal of Optimization Theory and Applications
Optimal and sub-optimal stopping rules for the Multistart algorithm in global optimization
Mathematical Programming: Series A and B
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
Noise strategies for improving local search
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
Global Optimization for Neural Network Training
Computer - Special issue: neural computing: companion issue to Spring 1996 IEEE Computational Science & Engineering
A Computing Procedure for Quantification Theory
Journal of the ACM (JACM)
Genetic Algorithms Plus Data Structures Equals Evolution Programs
Genetic Algorithms Plus Data Structures Equals Evolution Programs
Global Optimization for Satisfiability (SAT) Problem
IEEE Transactions on Knowledge and Data Engineering
Efficient Local Search with Conflict Minimization: A Case Study of the n-Queens Problem
IEEE Transactions on Knowledge and Data Engineering
Using Genetic Algorithms in Engineering Design Optimization with Non-Linear Constraints
Proceedings of the 5th International Conference on Genetic Algorithms
Efficient and adaptive Lagrange-multiplier methods for continuous nonlinear optimization
SAC '98 Proceedings of the 1998 ACM symposium on Applied Computing
Efficient and Adaptive Lagrange-Multiplier Methods for Nonlinear Continuous Global Optimization
Journal of Global Optimization
Global Optimization by Monotonic Transformation
Computational Optimization and Applications
Global Energy Minimization: A Transformation Approach
EMMCVPR '01 Proceedings of the Third International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition
Parallelizing simulated annealing algorithms based on high-performance computer
Journal of Global Optimization
Hi-index | 0.00 |
In this paper we present a method called NOVEL (Nonlinear Optimization via External Lead) forsolving continuous and discrete global optimization problems. NOVEL addresses the balance between global search and local search, using a trace to aid in identifying promising regions before committing to local searches. We discuss NOVEL for solving continuous constrained optimization problems and show how it can be extended to solve constrained satisfaction and discrete satisfiability problems. We first transform the problem using Lagrange multipliers into an unconstrained version. Since a stable solution in a Lagrangian formulation only guarantees a local optimum satisfying the constraints, we propose a global search phase in which an aperiodic and bounded trace function is added to the search to first identify promising regions for local search. The trace generates an information-bearing trajectory from which good starting points are identified for further local searches. Taking only a small portion of the total search time, this elegant approach significantly reduces unnecessary local searches in regions leading to the same local optimum. We demonstrate the effectiveness of NOVEL on a collection of continuous optimization benchmark problems, finding the same or better solutions while satisfying the constraints. We extend NOVEL to discrete constraint satisfaction problems (CPSs) by showing an efficient transformation method for CSPs and the associated representation in finite-difference equations in NOVEL. We apply NOVEL to solve Boolean satisfiability instances in circuit fault detection and circuit synthesis applications, and show comparable performance when compared to the best existing method.