## 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]}The task mapping problem of real-time applications onto a homogeneous multiple processors system-on-a-chip (MPSoC) is an NP-complete problem that can be addressed using search-based meta-heuristic methods.

^{[32]}The work is devoted to research and development of new meta-heuristic methods and algorithms of rational choice, planning and optimization of management decisions.

^{[33]}The solution techniques are mainly classified into mathematical programming algorithms, heuristics, meta-heuristic methods, and the analytical approaches.

^{[34]}The minimum control effort of PSO-GSA-tuned PID controller depicts the robust performance of the controller compared to other non-meta-heuristic and meta-heuristic methods presented.

^{[35]}We compare the split-window technique with meta-heuristic methods such as hill climbing, simulated annealing, line search, and genetic algorithms by running simulations on the data collected from a real-world hydropower river system in southern Norway.

^{[36]}The meta-heuristic methods, binary particle swarm optimization (BPSO) and binary grey wolf optimization (BGWO), are employed in this paper.

^{[37]}Finally, the parameters estimated by meta-heuristic and hybrid meta-heuristic methods are compared.

^{[38]}The results further showed that basic DE and improved DE with jump search are effective methods compared to the other meta-heuristic methods.

^{[39]}Further, the work covers the application of meta-heuristic methods concerning a proper fine-tune of these techniques.

^{[40]}In the past decade, although many meta-heuristic methods have been devoted to parameter estimation of PV models and achieved satisfactory results, they may suffer from consuming large computational resources to get promising performance.

^{[41]}Following extensive numerical experiments, and applications of fourteen well-known heuristic and meta-heuristic methods to solve seventy-one non-linear unconstrained and constrained, single-objective and multi-objective benchmarks, before and after receiving a boost, the OBA performance is investigated.

^{[42]}The majority of paper published in this research field use external hydraulic simulators and meta-heuristic methods to solve the optimization problem.

^{[43]}Various topology control algorithms have been proposed, but a few of them have used meta-heuristic methods such as genetic algorithms, neural networks, and learning automata.

^{[44]}To validate the outcomes of the proposed TLBO, we carry out an experimental study and compare its outcomes with the best-known results obtained by several meta-heuristic methods on a set of benchmark instances derived from the literature.

^{[45]}Since most of these problems are NP-hard ones, heuristic and meta-heuristic methods are used for solving these problems.

^{[46]}The most commonly used methods for solving classical (historical) ciphers are based on global optimization (meta-heuristic methods).

^{[47]}Since job rotation is categorized as nondeterministic polynomial-time hardness problem, meta-heuristic methods are used to solve it such as particle swarm optimization (PSO) algorithm.

^{[48]}Ant Colony Optimization is one of the meta-heuristic methods used to solve combinatorial optimization problems that are quite difficult.

^{[49]}As for optimisation, meta-heuristic methods are the preferred choice (57.

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## 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]}The meta-heuristic methods, binary particle swarm optimization (BPSO) and binary grey wolf optimization (BGWO), are employed in this paper.

^{[3]}Since job rotation is categorized as nondeterministic polynomial-time hardness problem, meta-heuristic methods are used to solve it such as particle swarm optimization (PSO) algorithm.

^{[4]}Design/methodology/approach : Pursuing mathematical approach and because the problem is NP-Hard, two meta-heuristic methods of Simulated Annealing (SA) and Particle Swarm Optimization (PSO) algorithms have been used.

^{[5]}