Abstract
The colony predation algorithm (CPA) is a new meta-heuristic algorithm that mimics the predation of animals to improve the radiation properties of antenna arrays. To overcome CPA’s problems of low accuracy and fast convergence, we propose an improved CPA called the chaotic colony predation algorithm (CCPA). The performance of CCPA was investigated in three parts. First, CCPA was tested with four benchmark functions. Then, CCPA was applied to equally spaced linear arrays to suppress the peak side-lobe level (PSLL) and place nulls in the desired directions. Finally, CCPA was used for a pattern synthesis of equally spaced linear arrays to reduce the PSLL under various constraints. Our results show that CCPA performed competitively with other well-known algorithms. Thus, CCPA is a promising option for solving electromagnetic optimization problems.
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