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1,248 claims from 785 papers are on the record. 45 have been checked so far; the other 1,203 have no check with a result yet.

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Status: Unchecked ×Topic: Metaheuristic Optimization Algorithms Research ×Clear all

18 claims from 13 papers

1. Computer Science › Metaheuristic Optimization Algorithms Research

   Grey Wolf Optimizer

   Mirjalili, Mirjalili and Lewis · Advances in Engineering Software · 2014

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   1. UncheckedThe Grey Wolf Optimizer algorithm gives results that compare well with other established meta-heuristic optimisation methods.“The results show that the GWO algorithm is able to provide very competitive results compared to these well-known meta-heuristics.”
   2. UncheckedThe paper states that its Grey Wolf Optimizer algorithm works on hard engineering problems where the search space is not known in advance.“The results of the classical engineering design problems and real application prove that the proposed algorithm is applicable to challenging problems with unknown search spaces.”

2. Computer Science › Metaheuristic Optimization Algorithms Research

   No free lunch theorems for optimization

   Wolpert and Macready · IEEE Transactions on Evolutionary Computation · 1997

   The paper builds a framework linking optimisation algorithms to the problems they solve, and presents 'no free lunch' theorems with applications to information theory, benchmarks and time-varying problems.

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   1. UncheckedFor any optimisation algorithm, doing better on one class of problems is paid for by doing worse on another class, according to the 'no free lunch' theorems.“A number of "no free lunch" (NFL) theorems are presented which establish that for any algorithm, any elevated performance over one class of problems is offset by performance over another class.”

3. Computer Science › Metaheuristic Optimization Algorithms Research

   Grasshopper Optimisation Algorithm: Theory and application

   Saremi, Mirjalili and Lewis · Advances in Engineering Software · 2017

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   1. UncheckedThe authors report that their proposed grasshopper-inspired optimisation algorithm gave better results than well-known and recent algorithms in the literature.“The results show that the proposed algorithm is able to provide superior results compared to well-known and recent algorithms in the literature.”

4. Computer Science › Metaheuristic Optimization Algorithms Research

   Hyper-heuristics: a survey of the state of the art

   Burke, Gendreau, Hyde et al. · Journal of the Operational Research Society · 2013

   A survey of the hyper-heuristics literature: its origins, the main types of approach, related areas, and current and future research directions.

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   1. UncheckedThe paper says hyper-heuristics fall into two main categories: those that select existing heuristics and those that generate new ones.“Two main hyper-heuristic categories can be considered: heuristic selection and heuristic generation.”
   2. UncheckedHyper-heuristics are defined by searching over heuristics or heuristic components, not directly over the possible solutions to the problem being addressed.“The distinguishing feature of hyper-heuristics is that they operate on a search space of heuristics (or heuristic components) rather than directly on the search space of solutions to the underlying problem that is being addressed.”

5. Computer Science › Metaheuristic Optimization Algorithms Research

   A novel nature-inspired algorithm for optimization: Squirrel search algorithm

   Jain, Singh and Rani · Swarm and Evolutionary Computation · 2018

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   1. UncheckedThe paper states that the squirrel search algorithm (SSA) gives more accurate solutions, and converges faster, than other existing optimisation methods.“The results demonstrate that SSA provides more accurate solutions with high convergence rate as compared to other existing optimizers.”

6. Computer Science › Metaheuristic Optimization Algorithms Research

   Lévy flight distribution: A new metaheuristic algorithm for solving engineering optimization problems

   Houssein, Saad, Hashim, Shaban and Hassaballah · Engineering Applications of Artificial Intelligence · 2020

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   1. UncheckedIn statistical simulations, the Lévy flight distribution (LFD) algorithm performed better than nine well-known metaheuristic algorithms in most tests.“The statistical simulation results revealed that the LFD algorithm provides better results with superior performance in most tests compared to several well-known metaheuristic algorithms such as simulated annealing (SA), differential evolution (DE), particle swarm optimization (PSO), elephant herdi…”
   2. UncheckedIn the paper's wireless sensor network simulations, the LFD algorithm reached a coverage rate of up to 43.16%, against up to 40% for A3, EECDS and CDS-Rule K.“Eventually, the LFD algorithm performs successfully achieving a high coverage rate up to 43.16 %, while the A3, EECDS, and CDS-Rule K algorithms achieve low coverage rates up to 40 % based on network sizes used in the simulation experiments.”

7. Computer Science › Metaheuristic Optimization Algorithms Research

   Golden eagle optimizer: A nature-inspired metaheuristic algorithm

   Mohammadi-Balani, Nayeri, Azar and Taghizadeh · Computers & Industrial Engineering · 2020

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   1. UncheckedThe paper says its multi-objective algorithm approximated true Pareto optimal solutions better than the two other multi-objective algorithms it was compared with.“Results were compared to that of two other multi-objective algorithms, which showed that it can approximate true Pareto optimal solutions better than the other two algorithms.”

8. Computer Science › Metaheuristic Optimization Algorithms Research

   Elk herd optimizer: a novel nature-inspired metaheuristic algorithm

   Al‐Betar, Awadallah, Braik, Makhadmeh and Doush · Artificial Intelligence Review · 2024

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   1. Unchecked“The comparative results were conducted against ten well-established metaheuristic algorithms and showed that the proposed EHO yielded the best results for almost all the benchmark functions used.”

9. Computer Science › Metaheuristic Optimization Algorithms Research

   A review of metaheuristic algorithms for solving TSP-based scheduling optimization problems

   Toaza and Esztergár‐Kiss · Applied Soft Computing · 2023

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   1. Unchecked“The results demonstrate that the most applied metaheuristic algorithm is the genetic algorithm, but the ant colony optimization algorithm is the most popular one based on the number of citations.”

10. Computer Science › Metaheuristic Optimization Algorithms Research

    Applications, classifications, and challenges: a comprehensive evaluation of recently developed metaheuristics for search and analysis

    Shaikh, Raj, Zheng et al. · Artificial Intelligence Review · 2025

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    1. Unchecked“The rise of such methods has led to redundancy and fragmentation in the field, as many reframe familiar optimization principles using superficial metaphors rather than advancing core algorithmic mechanisms.”

11. Computer Science › Metaheuristic Optimization Algorithms Research

    Evaluation of Large Language Models as Solution Generators in Complex Optimization

    Huang, Wu, Zhou et al. · IEEE Computational Intelligence Magazine · 2025

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    1. Unchecked“Specifically, despite their significant computational power, LLMs still significantly underperform in numerical optimization tasks, largely due to a mismatch between the problem domain and their processing capabilities.”

12. Computer Science › Metaheuristic Optimization Algorithms Research

    LLaMEA: A Large Language Model Evolutionary Algorithm for Automatically Generating Metaheuristics

    van Stein and Bäck · arXiv (Cornell University) · 2024

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    1. Unchecked“The algorithms also show competitive performance on the 10- and 20-dimensional instances of the test functions, although they have not seen such instances during the automated generation process.”

13. Computer Science › Metaheuristic Optimization Algorithms Research

    Efficient Heuristics Generation for Solving Combinatorial Optimization Problems Using Large Language Models

    Wu, Di Wang, Wu et al. · arXiv (Cornell University) · 2025

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    1. Unchecked“We theoretically prove the effectiveness of CAP in reducing unspecificity and provide empirical results in this work.”
    2. Unchecked“The use of PPP makes Hercules more resource-efficient and we name this variant Hercules-P.”
    3. Unchecked“Extensive experiments across four HG tasks, five COPs, and eight LLMs demonstrate that Hercules outperforms the state-of-the-art LLM-based HG algorithms, while Hercules-P excels at minimizing required computing resources.”