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In this paper, a new hybrid algorithm, Hybrid Symbiosis Organisms Search (HSOS) has been proposed by combining Symbiosis Organisms Search (SOS) algorithm with Simple Quadratic Interpolation (SQI). The proposed algorithm provides more efficient behavior when dealing with real-world and large scale problems. To verify the performance of this suggested algorithm, 13 (Thirteen) well known benchmark functions, CEC2005 and CEC2010 special session on real-parameter optimization are being considered. The results obtained by the proposed method are compared with other state-of-the-art algorithms and it was observed that the suggested approach provides an effective and efficient solution in regards to the quality of the final result as well as the convergence rate. Moreover, the effect of the common controlling parameters of the algorithm, viz. population size, number of fitness evaluations (number of generations) of the algorithm are also being investigated by considering different population sizes and the number of fitness evaluations (number of generations). Finally, the method endorsed in this paper has been applied to two real life problems and it was inferred that the output of the proposed algorithm is satisfactory.

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Differential evolution (DE) is an effective and powerful approach and it has been widely used in different environments. However, the performance of DE is sensitive to the choice of control parameters. Thus, to obtain optimal performance, time-consuming parameter tuning is necessary. Backtracking Search Optimization Algorithm (BSA) is a new evolutionary algorithm (EA) for solving real-valued numerical optimization problems. An ensemble algorithm called E-BSADE is proposed which incorporates concepts from DE and BSA. The performance of E-BSADE is evaluated on several benchmark functions and is compared with basic DE, BSA and conventional DE mutation strategy. Also the performance results are compared with state of the art PSO variant.

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