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Snake optimizer (SO) is an optimization algorithm drawn from the reproductive habits of serpents. It exhibits outstanding effectiveness in solving continuous optimization problems. However, SO may face some performance challenges related to its population diversity and early convergence behavior. In this paper, we address the challenges of SO by introducing the Hybrid snake optimizer algorithm (HSOA). HSOA is a novel approach to optimization that incorporates two new optimization techniques into the SO algorithm. First, it incorporates a new opposition-based learning technique called Oppositional-mutual learning into the initialization stage of the SO algorithm. Second, it integrates dynamic polynomial mutation, which is an intelligent mutation method, into the initialization and optimization stages of the SO algorithm. These integrated approaches aim to increase the population's diversity of SO, while improving its searchability during its optimization stage. In power systems, the economic load dispatch (ELD) is an intricate optimization problem that becomes more challenging when the restrictions of the valve point effect (VPE) are incorporated into it. ELD with VPE is non-convex, lacking smoothness, and exhibiting nonlinearity that considers operational limitations expressed as both equality and inequality constraints to generate electricity. The suggested HSOA algorithm underwent evaluation and was compared with 47 renowned optimization algorithms across five real-world ELD problems with different specifications: generators with different unit capacities, transmission losses, prohibited operation zones, and Ramp Rate restrictions. The experimental results demonstrate that HSOA produces competitive solutions for the five real-world ELD problems. In detail, HSOA achieves the top rank in three cases of ELD problems with a 3-unit generator, and it secures the second and third positions in high-dimensional ELD problems with 40-unit and 80-unit generators, respectively. The statistical tests confirm the reliability and efficiency of HSOA. In addition, the effectiveness of HSOA was evaluated using the single-objective IEEE-CEC 2014 functions and compared to the results of eight popular metaheuristic algorithms. The results demonstrate that HSOA is a competitive optimization algorithm capable of solving the functions of IEEE-CEC 2014. [ABSTRACT FROM AUTHOR]

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]

This paper presents a hybrid simplified grey wolf optimization algorithm combining with an modified pigeon inspired optimization algorithm. Firstly, a method of population initialization in the restricted area is designed to improve the average fitness of individuals. Then, a decay factor is designed to improve the search accuracy. At the same time, a new search position update formula is designed to balance the global search and local search ability, so as to improve the convergence speed of the algorithm. Furthermore, in order to verify the performance, the convergence, complexity and accuracy of the algorithm are analyzed by using linear difference equations. Finally, the comparison between different meta-heuristic optimization algorithms, the influence of parameters and their application in the path planning of unmanned aerial vehicle (UAV) are tested. Simulation results show that the proposed algorithm has stronger optimization ability and better robustness than those of compared algorithms. [ABSTRACT FROM AUTHOR]