Abstract: 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 accuracy and poor stability in the Whale Optimization Algorithm (WOA). Firstly, the golden sine operator 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 identified 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 increase 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 pressure vessels. LEDGWOA is compared against 17 other algorithms, demonstrating superior performance.

Key words: whale optimization algorithm, dominant whale group, golden sine, bi-directional local exploitation, chebyshev mapping