A novel chaotic chimp sine cosine algorithm Part-I: For solving optimization problem - Vigya; Raj, Saurav; Shiva, Chandan Kumar; Vedik, Basetti; Mahapatra, Sheila; Mukherjee, V. (Chaos, solitons and fractals, 2023)
In the realm of solving complex optimization problems, it becomes crucial to fulfill multiple objectives to achieve optimal system performance while adhering to various conditions and limitations. Traditional optimization methods often face challenges such as sluggish convergence speed and confinement to local optima, making it difficult to find satisfactory solution. To tackle this challenge, the development of multi-objective programming is directed towards satisfying physical constraints. In line of the above problems and limitations, this work aims to contribute by proposing a hybrid optimization technique that can effectively address such optimization issues. The proposed algorithm, called the chaotic chimp sine cosine (C-CHOA-SC) algorithm, combines the chimp optimization algorithm (CHOA) and the sine cosine algorithm (SCA) to overcome problems of slow convergence and getting stuck in local optima. It does this by using the SCA after the CHOA and incorporating chaos to explore and exploit the search space effectively. This hybrid approach aims to improve the algorithm's performance (offers enhanced exploration capabilities, efficient exploitation of the search space, and improved convergence speed) and find better solutions to optimization problems. These features make the C-CHOA-SC algorithm well-suited for solving complex optimization problems with multiple objectives, providing a contemporary and effective solution. To evaluate the performance of the proposed C-CHOA-SC algorithm, three phases of analysis are conducted. In the first phase, the exploration, exploitation, and avoidance of local optima are tested using the CEC 2019 benchmark problems. The second phase involves tracking and validating the performance of C-CHOA-SC using multiple performance metrics on two-dimensional test functions. Finally, non-parametric alternative test i.e., the Wilcoxon rank sum test and Friedman test are performed. The results demonstrate that the proposed technique exhibits significantly better performance compared to other methods, with a 95 % significance level. As concerned to Friedman test, the C-CHOA-SC algorithm obtains a lower rank, it implies that it outperforms the other competing algorithms in terms of its overall performance. •A new social-inspired optimization technique called C-CHOA-SCA has been proposed.•The efficacy and robustness of C-CHOA-SCA are tested on different benchmark functions.•The C-CHOA-SCA is examined by using qualitative and quantitative indices.•Wilcoxon test and Friedman test are used to verify the efficacy of UFIA.