Abstract: The dung beetle optimizer (DBO) is a metaheuristic algorithm with fast convergence and powerful search capabilities, which has shown excellent performance in solving various optimization problems. However, it suffers from the problems of easily falling into local optimal solutions and poor convergence accuracy when dealing with large-scale complex optimization problems. Therefore, we propose an adaptive DBO (ADBO) based on an elastic annealing mechanism to address these issues. First, the convergence factor is adjusted in a nonlinear decreasing manner to balance the requirements of global exploration and local exploitation, thus improving the convergence speed and search quality. Second, a greedy difference optimization strategy is introduced to increase population diversity, improve the global search capability, and avoid premature convergence. Finally, the elastic annealing mechanism is used to perturb the randomly selected individuals, helping the algorithm escape local optima and thereby improve solution quality and algorithm stability. The experimental results on the CEC 2017 and CEC 2022 benchmark function sets and MCNC benchmark circuits verify the effectiveness, superiority, and universality of ADBO.

一种基于弹性退火机制的自适应蜣螂优化器及其在数值问题和Reed–Muller逻辑电路优化中的应用

[1]Abdollahzadeh B, Gharehchopogh FS, Khodadadi N, et al., 2022. Mountain gazelle optimizer: a new nature-inspired metaheuristic algorithm for global optimization problems. Adv Eng Softw, 174:103282.

[2]Ahmadi-Javid A, 2011. Anarchic society optimization: a human-inspired method. IEEE Congress of Evolutionary Computation, p.2586-2592.

[3]Ali JM, Hussain MA, Tade MO, et al., 2015. Artificial intelligence techniques applied as estimator in chemical process systems–a literature survey. Expert Syst Appl, 42(14):5915-5931.

[4]Amores D, Tanin E, Vasardani M, 2024. Flexible paths: a path planning approach to dynamic navigation. IEEE Trans Intell Transp Syst, 25(6):4795-4808.

[5]Azizi M, Talatahari S, Gandomi AH, 2023. Fire hawk optimizer: a novel metaheuristic algorithm. Artif Intell Rev, 56(1):287-363.

[6]Dehghani M, Trojovský P, 2021. Teamwork optimization algorithm: a new optimization approach for function minimization/maximization. Sensors, 21(13):4567.

[7]Dehghani M, Montazeri Z, Trojovská E, et al., 2023. Coati optimization algorithm: a new bio-inspired metaheuristic algorithm for solving optimization problems. Knowl-Based Syst, 259:110011.

[8]El-Kenawy ESM, Khodadadi N, Mirjalili S, et al., 2024. Greylag goose optimization: nature-inspired optimization algorithm. Expert Syst Appl, 238:122147.

[9]Grefenstette JJ, 1993. Genetic algorithms and machine learning. Proc 6^th Annual Conf on Computational Learning Theory, p.3-4.

[10]Hama Rashid DN, Rashid TA, Mirjalili S, 2021. ANA: ant nesting algorithm for optimizing real-world problems. Mathematics, 9(23):3111.

[11]Hamad RK, Rashid TA, 2024. GOOSE algorithm: a powerful optimization tool for real-world engineering challenges and beyond. Evol Syst, 15(4):1249-1274.

[12]Han X, Meng ZL, Xia X, et al., 2024. Foundation intelligence for smart infrastructure services in Transportation 5.0. IEEE Trans Intell Veh, 9(1):39-47.

[13]He JC, Fu LH, 2024. Robot path planning based on improved dung beetle optimizer algorithm. J Braz Soc Mech Sci Eng, 46(4):235.

[14]He ZX, Pan YH, Wang KJ, et al., 2021. Area optimization for MPRM logic circuits based on improved multiple disturbances fireworks algorithm. Appl Math Comput, 399:126008.

[15]Heidari AA, Mirjalili S, Faris H, et al., 2019. Harris hawks optimization: algorithm and applications. Fut Gener Comput Syst, 97:849-872.

[16]Ikram RMA, Dehrashid AA, Zhang BQ, et al., 2023. A novel swarm intelligence: cuckoo optimization algorithm (COA) and SailFish optimizer (SFO) in landslide susceptibility assessment. Stoch Environ Res Risk Assess, 37(5):1717-1743.

[17]Kaur S, Awasthi LK, Sangal AL, et al., 2020. Tunicate swarm algorithm: a new bio-inspired based metaheuristic paradigm for global optimization. Eng Appl Artif Intell, 90:103541.

[18]Kaveh A, Dadras A, 2017. A novel metaheuristic optimization algorithm: thermal exchange optimization. Adv Eng Softw, 110:69-84.

[19]Kennedy J, Eberhart R, 1995. Particle swarm optimization. Proc Int Conf on Neural Networks, p.1942-1948.

[20]Kirkpatrick S, Gelatt CD Jr, Vecchi MP, 1983. Optimization by simulated annealing. Science, 220(4598):671-680.

[21]Li J, He Z, Wang SY, 2023. Advances in operation and finance in supply chains. Int J Prod Econ, 255:108707.

[22]Li LH, Liu LL, Shao YX, et al., 2023. Enhancing swarm intelligence for obstacle avoidance with multi-strategy and improved dung beetle optimization algorithm in mobile robot navigation. Electronics, 12(21):4462.

[23]Mirjalili S, Lewis A, 2016. The whale optimization algorithm. Adv Eng Softw, 95:51-67.

[24]Mirjalili S, Mirjalili SM, Lewis A, 2014. Grey wolf optimizer. Adv Eng Softw, 69:46-61.

[25]Peraza-Vázquez H, Peña-Delgado A, Merino-Treviño M, et al., 2024. A novel metaheuristic inspired by horned lizard defense tactics. Artif Intell Rev, 57(3):59.

[26]Rashedi E, Nezamabadi-Pour H, Saryazdi S, 2009. GSA: a gravitational search algorithm. Inform Sci, 179(13):2232-2248.

[27]Ray T, Liew KM, 2003. Society and civilization: an optimization algorithm based on the simulation of social behavior. IEEE Trans Evol Comput, 7(4):386-396.

[28]Reynolds RG, Peng B, 2004. Cultural algorithms: modeling of how cultures learn to solve problems. 16^th IEEE Int Conf on Tools with Artificial Intelligence, p.166-172.

[29]Romanov AM, 2024. Perfect mixed codes from generalized Reed–Muller codes. Des Codes Cryptogr, 92(6):1747-1759.

[30]Shao YX, He ZX, Zhou YH, et al., 2021. Area optimization of MPRM circuits based on M-AFSA. J Beijing Univ Aeronaut Astronaut, 49(3):693-701 (in Chinese).

[31]Shen QW, Zhang DM, Xie MS, et al., 2023. Multi-strategy enhanced dung beetle optimizer and its application in three-dimensional UAV path planning. Symmetry, 15(7):1432.

[32]Storn R, Price K, 1997. Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim, 11(4):341-359.

[33]Sun F, Wang PJ, Yu HZ, 2013. Best polarity searching for ternary FPRM logic circuit area based on whole annealing genetic algorithm. IEEE 10^th Int Conf on ASIC, p.1-4.

[34]Tan Y, Zhu YC, 2010. Fireworks algorithm for optimization. 1^st Int Conf on Advances in Swarm Intelligence, p.355-364.

[35]Wang LY, Cao QJ, Zhang ZX, et al., 2022. Artificial rabbits optimization: a new bio-inspired metaheuristic algorithm for solving engineering optimization problems. Eng Appl Artif Intell, 114:105082.

[36]Wang PJ, Li H, Wang ZH, 2010. MPRM expressions minimization based on simulated annealing genetic algorithm. IEEE Int Conf on Intelligent Systems and Knowledge Engineering, p.261-265.

[37]Xue JK, Shen B, 2023. Dung beetle optimizer: a new metaheuristic algorithm for global optimization. J Supercomput, 79(7):7305-7336.

[38]Zhou YH, He ZX, Liang XY, et al., 2021. Optimization of XNOR/OR circuit area based on BABFA. J Beijing Univ Aeronaut Astronaut, 48(10):2031-2039 (in Chinese).

[39]Zhou YH, He ZX, Jiang JH, et al., 2023. Fast area optimization approach for XNOR/OR-based fixed polarity Reed–Muller logic circuits based on multi-strategy wolf pack algorithm. ACM Trans Des Autom Electron Syst, 28(3):45.

[40]Zhu F, Li GS, Tang H, et al., 2024. Dung beetle optimization algorithm based on quantum computing and multi-strategy fusion for solving engineering problems. Expert Syst Appl, 236:121219.