Introduction to Meta Heuristic Methods

What is/are Meta Heuristic Methods?

Meta Heuristic Methods - Given that, finding the best answer is very important in meta-heuristic methods, we use the concept of dominance in the discussion of multi-objective optimization to find the best answers and show that, at low iterations, the performance of the NSGA II algorithm is better than the MOABC and MOACO algorithms in solving the portfolio optimization problem. ^[1] Eventually, the meta-heuristic methods of Genetic and shuffled frog-leaping algorithms are exploited to solve resulting PLC channel allocation problem via minimizing the interference. ^[2] Furthermore, the execution of the suggested algorithm with that of other meta-heuristic methods was contrasted. ^[3] The parameters of CL potentials can be optimized to any target quantity that can be computed using the potentials since meta-heuristic methods do not require the derivatives of the quantity with respect to parameters. ^[4] It is also found that FLPs are Non-deterministic Polynomial-complete problems, and hence, they set the challenges to researchers to develop efficient meta-heuristic methods to solve the bigger size FLPs in a reasonable time. ^[5] In order to improve the safety and efficiency of the robot in the process of moving, this paper proposes a new hybrid approach combining two meta-heuristic methods, we used particle swarm optimization (PSO)-based grey wolf optimization (GWO) to solve this problem. ^[6] Hybrid meta-heuristic methods are used in the optimization steps. ^[7] This paper also proposed three meta-heuristic methods including Multi-Objective Teaching–learning-based optimization (TLBO), Particle Swarm Optimization (PSO), and Genetic Algorithm (GA) to find Pareto solutions. ^[8] Four optimization meta-heuristic methods including a genetic algorithm (GA), imperialist competitive algorithm (ICA), election algorithm (EA), and gray wolf algorithm (GWO), based on the support vector regression method (SVR), were used to estimate the discharge coefficient (Cd) of vertically cosine shape weirs. ^[9] To solve the model in small and large dimensions, two exact methods (LP-metric and e-constraint) and two meta-heuristic methods (NSGA-II and MOPSO) are used. ^[10] Conclusion: Due to the limited energy of sensors, the use of meta-heuristic methods in clustering and routing improves network performance and increases the wireless sensor network's lifetime. ^[11] Most of existing mechanisms are heuristic and meta-heuristic methods, developed to address a part of scheduling problem and did not consider the dynamic creation of VMs by taking into account the required resources for a user task and the capabilities of a set of available hosts. ^[12] they are generally categorised as NP hard, a significant portion of the literature is dedicated to the development and performance of solution approaches spanning from exact and heuristics to recent meta-heuristic methods. ^[13] The use of meta-heuristic methods has shown satisfactory results so far. ^[14] Then, considering the NP-hardness of the problem, we solve it using two meta-heuristic methods, namely the non-dominated sorting genetic algorithm (NSGA-II) and the Bees algorithm. ^[15] In this paper, it is shown that AMFA can solve the UC problem in a better manner compared to the other meta-heuristic methods. ^[16] Nature-inspired problemsolving approaches include meta-heuristic methods that are focused on evolutionary computation and swarm intelligence. ^[17] The meta-heuristic methods are the promising approach to acquire the optimal network performance. ^[18] Meta-heuristic methods are commonly applied to difficult permutation type problems such as the Traveling Salesman Problem (TSP). ^[19] The present study aimed to use several classic and meta-heuristic methods to estimate these missing data. ^[20] Due to the NP-hardness of the problems, the studied case is solved with two meta-heuristic methods of NSGA II and SPEA II on large-scale instance problems and the Taguchi method is utilized to set the parameters of these two meta-heuristic algorithms. ^[21] Many meta-heuristic methods have been proposed to solve various combinatorial optimization problems. ^[22] Furthermore, different statistical parameters for these soft computing techniques like BPSO, ACO, and GWO have been presented and compared to assess the efficacy of these meta-heuristic methods. ^[23] Due to the complexity of this task, recently, meta-heuristic methods have been applied with promising results. ^[24] Finally, possible future research directions of meta-heuristic methods for solving LBOPs are proposed. ^[25] In the past several years, a variety of meta-heuristic methods were introduced to eliminate redundant and irrelevant features as much as possible from high-dimensional datasets. ^[26] The selected papers have been categorized into three main groups, decision-making methods (17 papers), meta-heuristic methods (8 papers) and fuzzy-based methods (7 papers). ^[27] Meanwhile, the contributions of self-adaptive strategies for attraction model and stochastic model are investigated with experimental analysis, respectively Finally, SAFA and other meta-heuristic methods are employed to solve constrained engineering design problems. ^[28] From the experimental results of AO that compared with well-known meta-heuristic methods, the superiority of the developed AO algorithm is observed. ^[29] In this study, optimization of an existing model in the literature with different meta-heuristic methods was further examined and results similar to those in the literature were obtained. ^[30] In such cases, solutions are often developed using heuristic or meta-heuristic methods. ^[31] In this research, Meta heuristic methods and sensitive index methods are used for determining the optimal location and sizing of custom power devices/FACTS devices. ^[32]

particle swarm optimization

In order to improve the safety and efficiency of the robot in the process of moving, this paper proposes a new hybrid approach combining two meta-heuristic methods, we used particle swarm optimization (PSO)-based grey wolf optimization (GWO) to solve this problem. ^[1] This paper also proposed three meta-heuristic methods including Multi-Objective Teaching–learning-based optimization (TLBO), Particle Swarm Optimization (PSO), and Genetic Algorithm (GA) to find Pareto solutions. ^[2]