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Arithmetic optimization algorithm (AOA) is a meta-heuristic optimization method based on mathematical operators proposed in recent years. Although it has good performance, it can also lead to insufficient local search ability and falling into local optima when solving complex optimization problems. In order to make up for the above shortcomings, the optimization performance of AOA is further improved. This paper proposes a hybrid algorithm based on AOA and particle swarm optimization (PSO) called HAOAPSO. Firstly, a compound opposition-based learning (COBL) strategy is introduced to broaden the scope of finding optimal solutions to help the algorithm better jump out of local optima. Secondly, PSO is combined with AOA that integrates COBL to improve the algorithm's local search ability, so as to improve the overall search efficiency of the algorithm. In addition, experiments are performed on 23 classical benchmark functions with different characteristics and five engineering design optimization problems, and the experimental results of HAOAPSO are compared with those of other well-known optimization algorithms to comprehensively evaluate the performance of the proposed algorithm. The simulation results show that HAOAPSO can provide better solutions in most cases when solving global optimization problems such as engineering, with better convergence speed and accuracy. [ABSTRACT FROM AUTHOR]

The marine predators algorithm (MPA) is a metaheuristic algorithm for solving optimization problems. MPA divides the whole optimization process into three phases evenly, and each phase corresponds to a different search agent update strategy. Such a setup makes MPA inflexible when facing different optimization problems, which affects the optimization performance. In this paper, we propose a novel modified MPA hybridizing by Q-learning (QMPA), which applies reinforcement learning to the selection of update strategy, and selects the most appropriate position update strategy for search agents in different iteration stages and states. It can effectively compensate for the deficiency of MPA's adaptive ability when facing different optimization problems. The performance of QMPA is tested on classical benchmark functions, the CEC2014 test suite, and engineering problems. In the classical benchmark functions test, QMPA is compared with MPA in 10, 30, and 50 dimensions. QMPA performs better than MPA for seven of the ten functions when the dimension is 10 and 30. The results of dimension 50 show that QMPA outperforms MPA in 5 functions and is close to it in 4 functions. Then, comparing QMPA with algorithms such as grey wolf optimizer, particle swarm optimization, slime mould algorithm, sine cosine algorithm, reptile search algorithm, and aquila optimizer, the results show that QMPA has the best performance on 22 of the total 30 functions in the CEC2014 test suite. Finally, QMPA is tested on two commonly used real-world engineering problems and gives the most optimal results. In general, the adaptive update strategy proposed in this paper improves the optimization performance of the MPA algorithm in terms of convergence and stability. [ABSTRACT FROM AUTHOR]

Energy is the most valuable resource in WSN. As the power storage capability of the tiny WSN is very limited, efficient mechanisms for energy minimization should be implemented to increase the overall lifetime of the network. Efficient clusterization of the area, cluster head selection leads toward the energy minimization of the whole network. Previously very few research works had been reported where the use of serve points had been considered. In this research work, the WSN has been configured depending upon many important parameters like (1) the frequency of serve points in a particular area and (2) WSN node deployment strategy in the target area. The main goal of this paper is to construct an efficient wireless sensor network. The efficiency of the network directly depends on the minimization technique of the consumed energy. So by minimizing the consumed energy an efficient wireless sensor network has been established. In this research, the specific region (target area) has been clustered using a modified K-mean algorithm to fix the position of the sink node. Deployment of WSN nodes in the target area has been done using least distance connect first (LDCF) deployment strategy. The cluster head has been selected for each cluster for each duty cycle. After configuring the WSN by using a modified k-mean algorithm and (LDCF) deployment strategy, a modified metaheuristic hybrid algorithm (GA-SAMP-MWPSO) has been used to get the optimized energy consumption for the network. Ultimately the consumed energy and the total day-life of the network have been calculated. The result has been compared with the existing literature, and it has been noted that the proposed technique yields 15.5544456 day-life of the network where the existing literature's approach gives 6.333777 day-life of the network. The proposed algorithm delivers a better outcome. [ABSTRACT FROM AUTHOR]