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This paper proposes the Love Evolution Algorithm (LEA), a novel evolutionary algorithm inspired by the stimulus–value–role theory. The optimization process of the LEA includes three phases: stimulus, value, and role. Both partners evolve through these phases and benefit from them regardless of the outcome of the relationship. This inspiration is abstracted into mathematical models for global optimization. The efficiency of the LEA is validated through numerical experiments with CEC2017 benchmark functions, outperforming seven metaheuristic algorithms as evidenced by the Wilcoxon signed-rank test and the Friedman test. Further tests using the CEC2022 benchmark functions confirm the competitiveness of the LEA compared to seven state-of-the-art metaheuristics. Lastly, the study extends to real-world problems, demonstrating the performance of the LEA across eight diverse engineering problems. Source codes of the LEA are publicly available at https://ww2.mathworks.cn/matlabcentral/fileexchange/159101-love-evolution-algorithm. [ABSTRACT FROM AUTHOR]

The democratization of smart devices over the last decade has given rise what is called the Internet of Thing (IoT). In view of the multitude of functionally equivalent services that have different quality of service (QoS) levels, the services composition is one of the main challenges in the IoT environments where several devices interact with each other to perform a user's complex task. This paper proposes a QoS-aware services composition approach that exploits a novel bat algorithm (QC-NBA) to compose the best IoT services while considering user's constraints related to the QoS properties. Unlike most existing bio-inspired services composition approaches, the NBA method includes mechanisms that improve the exploration and exploitation of the composition search space. The bats habitat selection, the Doppler Effect compensation and the self-adaptive local search strategy of the NBA method speed-up the convergence and avoid the local optimum, enhancing therefore the performance of the QC-NBA algorithm in term of execution time and composition quality. The results obtained through the simulation scenarios, show that the QC-NBA approach achieves a good composition in terms of QoS utility and converges faster compared to other services composition baselines. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is made up of intelligent devices that interact with each other. It allows information to be gathered and shared by these devices. In addition, IoT now has a wide range of life applications such as business, logistics, health monitoring, smart ecosystem, and information concerning individuals, social and private environments. Security-related issues involved in such a communication network are drastically increasing day by day. Therefore, existing authentication and authorization concepts must grow along with technological evolution. This paper presents an artificial intelligence (AI)-assisted Bio-inspired algorithm for secure IoT communication networks (AI-BIAS) to improve future communication. The proposed framework is categorized into two sections: First, the Bio-inspired algorithm-assisted blockchain technology for authentication and authorization in the IoT communication network is presented. Further, the Artificial Intelligence algorithm monitors the proposed IoT communication network. The experimental results show that the proposed model enhances the accuracy ratio of 98.3%, average latency of 26 ms, average cost of 25.7%, authentication time 0.04 ms, multistep prediction's MAPE, and RMSE values are 0.7645 and 0.9878 when compared to other popular methods. [ABSTRACT FROM AUTHOR]

Suspension bridges are critical components of transport infrastructure around the world. Therefore, their operating conditions should be effectively monitored to ensure their safety and reliability. However, the main cables of suspension bridges inevitably deteriorate over time due to corrosion, as a result of their operational and environmental conditions. Thus, accurate annual corrosion rate predictions are crucial for maintaining reliable structures and optimal maintenance operations. However, the corrosion rate is a chaotic and complex phenomenon with highly nonlinear behavior. This paper proposes a novel predictive model for the estimation of the annual corrosion rate in the main cables of suspension bridges. This is a hybrid model based on the multilayer perceptron (MLP) technique optimized using marine predators algorithm (MPA). In addition, well-known metaheuristic approaches such as the genetic algorithm (GA) and particle swarm algorithm (PSO) are employed to optimize the MLP. In order to implement the proposed model, a comprehensive database composed of 309 sample tests on the annual corrosion rate from all around the world, including various factors related to the surrounding environmental properties, is utilized. In addition, several input combinations are proposed for investigating the trigger factors in modeling the annual corrosion rate. The performance of the proposed models is evaluated using various statistical and graphical criteria. The results of this study demonstrate that the proposed hybrid MLP-MPA model provides stable and accurate predictions, while it transcends the previously developed approaches for solving this problem. The effectiveness of the MLP-MPA model shows that it can be used for further studies on the reliability analysis of the main cables of suspension bridges. [ABSTRACT FROM AUTHOR]