Multi-strategy Remora Optimization Algorithm for solving multi-extremum problems

Jia Heming,LI YONGCHAO,Wu Di,Rao Honghua,Wen Changsheng,Abualigah Laith 한국CDE학회 2023 Journal of computational design and engineering Vol.10 No.4

A metaheuristic algorithm that simulates the foraging behavior of remora has been proposed in recent years, called ROA. ROA mainly simulates host parasitism and host switching in the foraging behavior of remora. However, in the experiment, it was found that there is still room for improvement in the performance of ROA. When dealing with complex optimization problems, ROA often falls into local optimal solutions, and there is also the problem of too-slow convergence. Inspired by the natural rule of “Survival of the fittest”, this paper proposes a random restart strategy to improve the ability of ROA to jump out of the local optimal solution. Secondly, inspired by the foraging behavior of remora, this paper adds an information entropy evaluation strategy and visual perception strategy based on ROA. With the blessing of three strategies, a multi-strategy Remora Optimization Algorithm (MSROA) is proposed. Through 23 benchmark functions and IEEE CEC2017 test functions, MSROA is comprehensively tested, and the experimental results show that MSROA has strong optimization capabilities. In order to further verify the application of MSROA in practice, this paper tests MSROA through five practical engineering problems, which proves that MSROA has strong competitiveness in solving practical optimization problems.

An Improved Reptile Search Algorithm with Ghost Opposition-based Learning for Global Optimization Problems

Jia Heming,Lu Chenghao,Wu Di,Wen Changsheng,Rao Honghua,Abualigah Laith 한국CDE학회 2023 Journal of computational design and engineering Vol.10 No.4

In 2021, a meta-heuristic algorithm, Reptile Search Algorithm (RSA), was proposed. RSA mainly simulates the cooperative predatory behavior of crocodiles. Although RSA has a fast convergence speed, due to the influence of the crocodile predation mechanism, if the algorithm falls into the local optimum in the early stage, RSA will probably be unable to jump out of the local optimum, resulting in a poor comprehensive performance. Because of the shortcomings of RSA, introducing the local escape operator can effectively improve crocodiles' ability to explore space and generate new crocodiles to replace poor crocodiles. Benefiting from adding a restart strategy, when the optimal solution of RSA is no longer updated, the algorithm’s ability to jump out of the local optimum is effectively improved by randomly initializing the crocodile. Then joining Ghost opposition-based learning to balance the IRSA’s exploitation and exploration, the Improved RSA with Ghost Opposition-based Learning for the Global Optimization Problem (IRSA) is proposed. To verify the performance of IRSA, we used nine famous optimization algorithms to compare with IRSA in 23 standard benchmark functions and CEC2020 test functions. The experiments show that IRSA has good optimization performance and robustness, and can effectively solve six classical engineering problems, thus proving its effectiveness in solving practical problems.

Improved snow ablation optimizer with heat transfer and condensation strategy for global optimization problem

Jia Heming,You Fangkai,Wu Di,Rao Honghua,Wu Hangqu,Abualigah Laith 한국CDE학회 2023 Journal of computational design and engineering Vol.10 No.6

The snow ablation optimizer (SAO) is a new metaheuristic algorithm proposed in April 2023. It simulates the phenomenon of snow sublimation and melting in nature and has a good optimization effect. The SAO proposes a new two-population mechanism. By introducing Brownian motion to simulate the random motion of gas molecules in space. However, as the temperature factor changes, most water molecules are converted into water vapor, which breaks the balance between exploration and exploitation, and reduces the optimization ability of the algorithm in the later stage. Especially in the face of high-dimensional problems, it is easy to fall into local optimal. In order to improve the efficiency of the algorithm, this paper proposes an improved snow ablation optimizer with heat transfer and condensation strategy (SAOHTC). Firstly, this article proposes a heat transfer strategy, which utilizes gas molecules to transfer heat from high to low temperatures and move their positions from low to high temperatures, causing individuals with lower fitness in the population to move towards individuals with higher fitness, thereby improving the optimization efficiency of the original algorithm. Secondly, a condensation strategy is proposed, which can transform water vapor into water by simulating condensation in nature, improve the deficiency of the original two-population mechanism, and improve the convergence speed. Finally, to verify the performance of SAOHTC, in this paper, two benchmark experiments of IEEE CEC2014 and IEEE CEC2017 and five engineering problems are used to test the superior performance of SAOHTC.

Improve coati optimization algorithm for solving constrained engineering optimization problems

Jia Heming,Shi Shengzhao,Wu Di,Rao Honghua,Zhang Jinrui,Abualigah Laith 한국CDE학회 2023 Journal of computational design and engineering Vol.10 No.6

The coati optimization algorithm (COA) is a meta-heuristic optimization algorithm proposed in 2022. It creates mathematical models according to the habits and social behaviors of coatis: (i) In the group organization of the coatis, half of the coatis climb trees to chase their prey away, while the other half wait beneath to catch it and (ii) Coatis avoidance predators behavior, which gives the algorithm strong global exploration ability. However, over the course of our experiment, we uncovered opportunities for enhancing the algorithm’s performance. When confronted with intricate optimization problems, certain limitations surfaced. Much like a long-nosed raccoon gradually narrowing its search range as it approaches the optimal solution, COA algorithm exhibited tendencies that could result in reduced convergence speed and the risk of becoming trapped in local optima. In this paper, we propose an improved coati optimization algorithm (ICOA) to enhance the algorithm’s efficiency. Through a sound-based search envelopment strategy, coatis can capture prey more quickly and accurately, allowing the algorithm to converge more rapidly. By employing a physical exertion strategy, coatis can have a greater variety of escape options when being chased, thereby enhancing the algorithm’s exploratory capabilities and the ability to escape local optima. Finally, the lens opposition-based learning strategy is added to improve the algorithm’s global performance. To validate the performance of the ICOA, we conducted tests using the IEEE CEC2014 and IEEE CEC2017 benchmark functions, as well as six engineering problems.

Enhanced Catalytic Ability of Candida rugosa Lipase Immobilized on Pore-enlarged Hollow Silica Microspheres and Cross-linked by Modified Dextran in Both Aqueous and Non-aqueous Phases

Liwei Ren,Honghua Jia,Min Yu,Wenzhong Shen,Hua Zhou,Ping Wei 한국생물공학회 2013 Biotechnology and Bioprocess Engineering Vol.18 No.5

Candida rugosa lipase (CRL) is one of the most widely used lipases. To enhance the catalytic abilities of CRL in both aqueous and non-aqueous phases, hollow silica microspheres (HSMSs) with a pore size of 18.07 nm were used as an immobilization support, and aldehydecontaining dextrans were employed to further cross-link the adsorbed CRL. In the experimental ranges examined,the loading amount of lipase linearly increased to 171 ±3.4 mgprotein/gsupport with the CRL concentration and all the adsorption equilibriums were reached within 30 min. After simple cross-linking, the tolerance to pH 4.0 ~ 8.0 as well as the thermal stability of immobilized CRL at 40 ~ 80oC were both substantially increased, and 82 ± 2.1% activity remaining after the sixth reuse. The immobilized CRL was successfully applied to the resolution of racemic ibuprofen in non-aqueous phase. The initial reaction rate increased by 1.4- and 3.6-fold compared with the rates of adsorbed and native lipases, respectively. Furthermore, the R-ibuprofen was obtained at ee > 93%, and the enantiomeric ratio reached E > 140 at the conversion of 50 ± 1.5% within 48 h.