Abstract
To address the issue of low efficiency in source seeking within implicit information fields, this paper proposes an autonomous sourcing method based on a balanced search strategy inspired by biological homing behaviors. At the outset of the research, the task of source seeking boiled down to a multi-objective convergence problem. By utilizing feasibility search behaviors as individual samples in evolutionary population, drawing on the principles of evolutionary algorithms, motion searching was integrated with population evolution to guide carriers towards completing source seeking tasks by solving multi-objective problems. Furthermore, the distribution entropy was also considered to measure the searching bias in the process of source seeking. In combination with the requirements of the source seeking process, a new method for balanced searching was designed. Ultimately, through theoretical analysis and simulation verification, we confirmed the effectiveness and rationality of this proposed method.
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