Self organizing migrating algorithm with quadratic interpolation for solving large scale global optimization problems

• In general, complexity of large scale problems increases with rise in dimension.
• Large scale problems require large population size and computational cost.
• In this paper SOMAQI has been used to solve large scale problems (dim. 100 to 3000).
• Only a population size 10 is required to solve all dimensional problems.
• It can be considered as low computational cost technique.
• It converges fast as compared to other techniques.

Generally the complexity of the large scale optimization problem is considered to increase as the size or dimension of the problem increases and to solve these problems; more efficient and robust algorithms are needed. Several experiments have shown that an increment in dimensions of the problem not only requires an increment in population size but increases the computational cost also. In this paper a Self Organizing Migrating Algorithm with Quadratic Interpolation (SOMAQI) has been extended to solve large scale global optimization problems for dimensions ranging from 100 to 3000 with a constant population size of 10 only. It produces high quality optimal solution with very low computational cost and converges very fast to optimal solution.