A collection of test problems for constrained global optimization algorithms
A collection of test problems for constrained global optimization algorithms
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
New ideas in optimization
Advances in Engineering Software
New Sequential and Parallel Derivative-Free Algorithms for Unconstrained Minimization
SIAM Journal on Optimization
A Numerical Comparison of Some Modified Controlled Random SearchAlgorithms
Journal of Global Optimization
Stochastic Methods for Practical Global Optimization
Journal of Global Optimization
On Uniform Covering, Adaptive Random Search and Raspberries
Journal of Global Optimization
On success rates for controlled random search
Journal of Global Optimization
A novel metaheuristics approach for continuous global optimization
Journal of Global Optimization
How to Solve It: Modern Heuristics
How to Solve It: Modern Heuristics
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The paper addresses a random search optimization method for nonlinear problems with continuous variables. The approach, called LJ-MM algorithm, deals with both unconstrained and constrained optimization problems. The algorithm was developed on the basis of the so called Luus-Jaakola (LJ) one, which was successfully used by several researchers to solve chemical and process engineering problems. The LJ-MM approach is aimed at highly multi-modal problems with sharp peaks. The major change in comparison with the LJ algorithm consists in different scheme of search space reduction rate. The tests carried out for several unconstrained and constrained problems proved its high performance for multi-modal problems with sharp peaks in particular. Also, they showed that it is the robust solver even in cases of problems with a smoother function. In all cases the performance of the LJ-MM approach depends only slightly on starting points and parameter setting. The detailed analysis of the test results and the comparison with the original LJ algorithm and others stochastic solvers is given in the paper.