On combining feasibility, descent and superlinear convergence in inequality constrained optimization
Mathematical Programming: Series A and B
Tabu search for nonlinear and parametric optimization (with links to genetic algorithms)
Discrete Applied Mathematics - Special volume: viewpoints on optimization
Algorithm 573: NL2SOL—An Adaptive Nonlinear Least-Squares Algorithm [E4]
ACM Transactions on Mathematical Software (TOMS)
SNOPT: An SQP Algorithm for Large-Scale Constrained Optimization
SIAM Journal on Optimization
Journal of Global Optimization
A Template for Scatter Search and Path Relinking
AE '97 Selected Papers from the Third European Conference on Artificial Evolution
Scatter Search: Methodology and Implementations in C
Scatter Search: Methodology and Implementations in C
Pattern search algorithms for mixed variable general constrained optimization problems
Pattern search algorithms for mixed variable general constrained optimization problems
Journal of Global Optimization
Search biases in constrained evolutionary optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Stochastic ranking for constrained evolutionary optimization
IEEE Transactions on Evolutionary Computation
Extended ant colony optimization for non-convex mixed integer nonlinear programming
Computers and Operations Research
Improved scatter search for the global optimization of computationally expensive dynamic models
Journal of Global Optimization
An evolutionary method for complex-process optimization
Computers and Operations Research
A review of recent advances in global optimization
Journal of Global Optimization
Dynamic multi-compartmental modelling of metal bioaccumulation in fish: Identifiability implications
Environmental Modelling & Software
Journal of Global Optimization
Parameter Estimation Using Metaheuristics in Systems Biology: A Comprehensive Review
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Surrogate-assisted evolutionary programming for high dimensional constrained black-box optimization
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
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Scatter search is a population-based method that has recently been shown to yield promising outcomes for solving combinatorial and nonlinear optimization problems. Based on formulations originally proposed in 1960s for combining decision rules and problem constraints such as the surrogate constraint method, scatter search uses strategies for combining solution vectors that have proved effective in a variety of problem settings. In this paper, we develop a general purpose heuristic for a class of nonlinear optimization problems. The procedure is based on the scatter search methodology and treats the objective function evaluation as a black box, making the search algorithm context-independent. Most optimization problems in the chemical and bio-chemical industries are highly nonlinear in either the objective function or the constraints. Moreover, they usually present differential-algebraic systems of constraints. In this type of problem, the evaluation of a solution or even the feasibility test of a set of values for the decision variables is a time-consuming operation. In this context, the solution method is limited to a reduced number of solution examinations. We have implemented a scatter search procedure in Matlab (Mathworks, 2004) for this special class of difficult optimization problems. Our development goes beyond a simple exercise of applying scatter search to this class of problems, but presents innovative mechanisms to obtain a good balance between intensification and diversification in a short-term search horizon. Computational comparisons with other recent methods over a set of benchmark problems favor the proposed procedure.