The Coati Optimization Algorithm (COA) is a newly developed metaheuristic algorithm, drawing inspiration from the clever tactics Coatis use when attacking Iguanas as well as their strategies for dealing with and evading predators. This algorithm has shown a commendable level of effectiveness when compared to various other metaheuristic algorithms. Its performance metrics indicate that it outperforms many alternatives in terms of efficiency and results. To overcome challenges such as the imbalance between exploration and exploitation phases and become trapped in local optima for solving complex optimization problems, an innovative technique known as "Enhanced Opposition-Based Learning" (EOBL) has been integrated with the COA algorithm. This technique draws inspiration from Random Opposition-Based Learning methods and can effectively influence the balance between exploration and exploitation phases. The Enhanced of Coati Optimization Algorithm (EOBCOA) is a novel metaheuristic algorithm proposed to enhance the performance of the COA. This method has been applied on standard benchmark functions to improve the proposed optimization algorithm. To assess the effectiveness of the proposed EOBCOA method, it was tested on standard benchmark functions, including IEEE CEC2005, IEEE CEC2019, and seven engineering problems. The results show that the EOBCOA method outperforms other advanced algorithms in achieving global optimization.

During the scheduling process, it is important to respect the constraints given by the jobs and the cloud providers. In addition to maintaining a balance between Quality of Service (QoS), fairness, and efficiency of jobs, scheduling is challenging. This paper aims to propose an efficient algorithm for load-balanced task scheduling in the cloud. Our algorithm uses a new meta-heuristic algorithm called COA (Coati Optimization Algorithm) to solve the task scheduling problem. This method is called COTSA (Coati Optimization-based Task Scheduling Algorithm). Its main goal is to reduce execution costs, load balancing, resource consumption, and makepan. Additionally, experimental results indicate that COTSA contributes to reduced energy consumption and enhanced system scalability and fault tolerance under simulated conditions. These improvements suggest potential suitability for dynamic and large-scale cloud infrastructures, though performance may vary depending on workload characteristics and system configurations. It is compared with Walrus Optimizer (WO), Slap Swarm Algorithm (SSA), Whale Optimization Algorithm (WOA), Zebra Optimization Algorithms (ZOA), Grasshopper Optimization Algorithm (GOA), Sooty Tern Optimization Algorithm (STOA), Golden Eagle Optimizer (GEO), Grey Wolf Optimizer (GWO), Subtraction-Average-Based Optimizer (SABO), and Sand Cat Swarm Optimization (SCSO), which are popular meta-heuristics. Experimental results demonstrate that COTSA reduces makespan by approximately 9%, lowers execution cost by up to 40%, improves resource utilization by around 3%, and enhances load balance by up to 30%, energy consumption about 36%, scalability near 17%, and fault tolerance about 16%, making it a robust and scalable solution for efficient cloud task scheduling.

Numerous algorithms have recently been invented with varying strengths and weaknesses, none of which is the best for all cases. Herein, a hybrid optimization method known as a PSOHHO optimization algorithm is presented. There are two methods for combining algorithms: parallel and sequential. We adopted the parallel method and optimized the algorithm's performance. We cover the weaknesses of one algorithm with the strengths of another algorithm using a new method of combination. In this method, using several formulas, the top populations are exchanged between the two algorithms, and a new population is created. With this ability, the strengths of an algorithm can be used to compensate for the weaknesses of the other algorithm. In this method, no changes are made to the algorithms. The main goal is to use existing algorithms. This method aims to attain the optimal solution in the shortest time possible. Two algorithms of particle swarm optimization (PSO) and Harris Hawks optimization (HHO) were used to present this method and five truss samples were considered to confirm the performance of this method. Based on the results, this method has rapid convergence speed and acceptable results compared to the other methods. It also yields better results than its basic algorithms.

The Fruit Fly Optimization algorithm is an intelligent optimization algorithm. To improve accuracy, convergence speed, as well as jumping out of local optimum, a modified Fruit Fly Optimization algorithm (MFFOV) is proposed in this paper. The proposed algorithm uses velocity in particle swarm optimization and improves smell based on dimension and random perturbations. As a result of testing ten benchmark functions, the convergence speed and accuracy are clearly improved in Modified Fruit Fly Optimization (MFFOV) compared to algorithms of Fruit Fly Optimization (FFO), Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), Teaching-Learning-Based Optimization (TLBO), Genetic Algorithms (GA), Gravitational Search Algorithms (GSA), Differential Evaluations (DEs) and Hunter–Prey Optimizations (HPOs). A performance verification algorithm is also proposed and applied to two engineering problems. Test functions and engineering problems were successfully solved by the proposed algorithm.