INFO is a metaphor-free
decentralized optimization method built to enable scalable, powerful, user-friendly
methods for the artificial inteligence community.
This study presents the analysis and principle of an innovative optimizer named weighted meaN oF vectOrs (INFO) to optimize different problems. INFO is a modified weight mean method, whereby the weighted mean idea is employed for a solid structure and updating the vectors’ position using three core procedures: updating rule, vector combining, and a local search. The updating rule stage is based on a mean-based law and convergence acceleration to generate new vectors. The vector combining stage creates a combination of obtained vectors with the updating rule to achieve a promising solution. The updating rule and vector combining steps were improved in INFO to increase the exploration and exploitation capacities. Moreover, the local search stage helps this algorithm escape low-accuracy solutions and improve exploitation and convergence. The performance of INFO was evaluated in 48 mathematical test functions and five constrained engineering test cases. According to the literature, the results demonstrate that INFO outperforms other basic and advanced methods in terms of exploration and exploitation. In the case of engineering problems, the results indicate that the INFO can converge to 0.99% of the global optimum solution. Hence, the INFO algorithm is a promising tool for optimal designs in optimization problems, which stems from the considerable efficiency of this algorithm for optimizing constrained cases. The source codes of this algorithm will be publicly available at https://aliasgharheidari.com.
INFO is an easy and simple optimizer with secure basis and no metaphor that can be used for any class of problems. You just use the codes in your software, add your objective function, and test it.
- The proposed mean rule
combines the weighted mean of two sets of vectors
(a set of random vectors and another with local best, better, and worst
vectors)
as a strategy to promote exploration ability
- The proposed updating
rule operator updates vectors' position using the mean
rule and convergence acceleration (CA) part, which guarantees the search
ability and convergence speed of INFO.
- The scaling rate parameter
can balance the exploration and exploitation
search ability.
- To calculate the weighted mean of vectors, a
wavelet function is considered to
obtain vectors' weight, which allows the algorithm to search the
solution space
globally.
- The proposed vector combining operator combines global
exploration and
local exploitation phases to promote the search ability and escape from
local
optima.
- To ensure avoidance of locally optimal solutions, the
proposed local search
operator is included.
- The convergence speed of INFO is very
promising because the positions of
vectors always tend to move toward the regions with better solutions.
The PDF files of the INFO paper is available for download
The paper is online in elsevier in this link
Start building
your enhanced or hybrid algorithm with INFO
INFO: An Efficient Optimization
Algorithm based on Weighted Mean of Vectors
Expert Systems with
Applications, 116516, 2022
https://doi.org/10.1016/j.eswa.2022.116516