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Multiple evaluation metrics including correlation relationship, Number of Pixel Change Rate (NPCR), Unified Average Changing Intensity (UACI), and information entropy are collaboratively optimized by the ABC algorithm. The proposed method begins with the application of the SHA-256 algorithm to generate keys and random sequences using chaotic systems. These sequences are then employed for shuffling, DNA coding, decoding, and diffusion, generating initial encrypted images. Subsequently, the encrypted images serve as individuals within the ABC algorithm to determine optimal parameters of the chaotic systems and the best ciphertext image. Simulation experiments demonstrate that the ciphertext images achieved excellent results in information entropy, pixel correlation coefficient, NPCR, and UACI. The integration of the multiobjective ABC optimization algorithm with DNA coding in our proposed image encryption scheme results in heightened security, as evidenced by superior performance in various metrics. <\/jats:p>","DOI":"10.1142\/s0218127424500573","type":"journal-article","created":{"date-parts":[[2024,4,9]],"date-time":"2024-04-09T08:35:51Z","timestamp":1712651751000},"source":"Crossref","is-referenced-by-count":0,"title":["Multiobjective Optimization of Chaotic Image Encryption Based on ABC Algorithm and DNA Coding"],"prefix":"10.1142","volume":"34","author":[{"ORCID":"http:\/\/orcid.org\/0000-0002-9969-2697","authenticated-orcid":false,"given":"Jinwei","family":"Yu","sequence":"first","affiliation":[{"name":"School of Automation Science and Engineering, South China University of Technology, Guangzhou 510641, P. R. 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