TY - JOUR
T1 - Improved slime mould algorithm by opposition-based learning and Levy flight distribution for global optimization and advances in real-world engineering problems
AU - Abualigah, Laith
AU - Diabat, Ali
AU - Elaziz, Mohamed Abd
N1 - Publisher Copyright:
© 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.
PY - 2023/2
Y1 - 2023/2
N2 - Slime Mould Algorithm (SMA) is a recently introduced meta-heuristic stochastic method, which simulates the bio-oscillator of slime mould. In this paper, an improved variant of SMA is proposed, called OBLSMAL, to relieve the conventional method’s main weaknesses that converge fast/slow and fall in the local optima trap when dealing with complex and high dimensional problems. Two search strategies are added to conventional SMA. Firstly, opposition-based learning (OBL) is employed to improve the convergence speed of the SMA. Secondly, the Levy flight distribution (LFD) is used to enhance the ability of the exploration and exploitation searches during the early and later stages, respectively. The integrated two search methods significantly improve the convergence behavior and the searchability of the conventional SMA. The performance of the proposed OBLSMAL method is comprehensively investigated and analyzed by using (1) twenty-three classical benchmark functions such as unimodal, multi-modal, and fixed multi-modal, (2) ten IEEE CEC2019 benchmark functions, and (3) five common engineering design problems. The experimental results demonstrate that the search strategies of SMA and its convergence behavior are significantly developed. The proposed OBLSMAL achieves promising results, and it gets better performance compared to other well-known optimization methods.
AB - Slime Mould Algorithm (SMA) is a recently introduced meta-heuristic stochastic method, which simulates the bio-oscillator of slime mould. In this paper, an improved variant of SMA is proposed, called OBLSMAL, to relieve the conventional method’s main weaknesses that converge fast/slow and fall in the local optima trap when dealing with complex and high dimensional problems. Two search strategies are added to conventional SMA. Firstly, opposition-based learning (OBL) is employed to improve the convergence speed of the SMA. Secondly, the Levy flight distribution (LFD) is used to enhance the ability of the exploration and exploitation searches during the early and later stages, respectively. The integrated two search methods significantly improve the convergence behavior and the searchability of the conventional SMA. The performance of the proposed OBLSMAL method is comprehensively investigated and analyzed by using (1) twenty-three classical benchmark functions such as unimodal, multi-modal, and fixed multi-modal, (2) ten IEEE CEC2019 benchmark functions, and (3) five common engineering design problems. The experimental results demonstrate that the search strategies of SMA and its convergence behavior are significantly developed. The proposed OBLSMAL achieves promising results, and it gets better performance compared to other well-known optimization methods.
KW - Levy flight distribution
KW - Meta-heuristic optimization algorithms
KW - Opposition-based learning
KW - Real-world optimization problems
KW - Slime mould algorithm
UR - http://www.scopus.com/inward/record.url?scp=85117001311&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85117001311&partnerID=8YFLogxK
U2 - 10.1007/s12652-021-03372-w
DO - 10.1007/s12652-021-03372-w
M3 - Article
AN - SCOPUS:85117001311
SN - 1868-5137
VL - 14
SP - 1163
EP - 1202
JO - Journal of Ambient Intelligence and Humanized Computing
JF - Journal of Ambient Intelligence and Humanized Computing
IS - 2
ER -