A heterogeneous guided whale optimization algorithm based on forward-reverse local exploitation and the golden sine algorithm
The paper proposes a heterogeneous guided whale optimization algorithm(LEDGWOA)based on forward-reverse local exploitation and the golden sine algorithm to address the issues of low ac-curacy and poor stability in the Whale Optimization Algorithm(WOA).Firstly,the golden sine opera-tor is embedded during the prey searching phase,enhancing the intensity of information exchange among individuals based on the principle of"better and closer."Additionally,dominant whale groups are iden-tified based on fitness values,and an adaptive inertial weight is calculated to determine a virtual leader.During the prey encircling phase,a bidirectional exploitation strategy incorporating Chebyshev threshold is integrated to strengthen neighborhood development intensity.Random spiral updates indirectly in-crease population diversity in later iterations.The improved algorithm is evaluated through simulation experiments on CEC2017 and CEC2019 functions and successfully applied to optimize the design of pres-sure vessels.LEDGWOA is compared against 17 other algorithms,demonstrating superior perform-ance.
whale optimization algorithmdominant whale groupgolden sinebi-directional local ex-ploitationchebyshev mapping
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