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Novel Metaheuristic: Spy Algorithm
2022
IEICE transactions on information and systems
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We proposed a population-based metaheuristic called the spy algorithm for solving optimization problems and evaluated its performance. ...
The spy algorithm had the best accuracy and detected more global optimum points within less computation time, indicating that our spy algorithm is more robust and faster then these other algorithms. ...
The shortest computation time of Spy2 could be easily understood because Spy2 used more SwingMove and less MoveToward than Spy1. ...
doi:10.1587/transinf.2021edp7092
fatcat:2ozurehajjh2jfyfdixhdmzivu
Metaheuristic Optimization Algorithms
[chapter]
2019
Multi-Objective Optimization In Theory and Practice II: Metaheuristic Algorithms
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Thus, an initial feasible solution gauss for gradient based optimization algorithms can be generated with well known population based metaheuristic Genetic Algorithm. ...
But metaheuristic global optimization algorithms are less susceptible to discontinuity and differentiability and also bad proposals of initial feasible solution do not affect the end solution. ...
Population size: how many chromosomes are in population (in one generation). ...
doi:10.2174/9781681087054119010004
fatcat:7qgotacswnan7n7l7ku3zc5mv4
THE DAWN OF METAHEURISTIC ALGORITHMS
2018
International Journal of Software Engineering and Computer Systems
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This paper presents a foundational discussion on metaheuristic algorithms as a necessary ingredient in successful optimization endeavors and concludes, after analysis of some metaheuristic algorithms that ...
a good metaheuristic algorithm should consist of four components, namely global search, local search, randomization and identification of the best solution at each iteration. ...
ACKNOWLEDGEMENT The author appreciates the support of the Department of Mathematical Sciences, Faculty of Natural and Applied Sciences, Anchor University Lagos in funding this research work. ...
doi:10.15282/ijsecs.4.2.2018.4.0048
fatcat:np2dahmksjbxhegdr6wckr7p7q
Mitigating Metaphors: A Comprehensible Guide to Recent Nature-Inspired Algorithms
[article]
2019
arXiv
pre-print
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It also discusses commonalities between these algorithms and more classical nature-inspired metaheuristics such as evolutionary algorithms and particle swarm optimisation. ...
This, in turn, makes it difficult to both comprehend these algorithms and understand their relationships to other metaheuristics. ...
This can be seen in their citation counts: the 32 algorithms reviewed in this paper each have more than 200 citations; a third of them have more than 1000 citations. ...
arXiv:1902.08001v1
fatcat:473bjfmi6rax3c2cdj6dn3iioi
Cheetah chase algorithm (CCA): a nature-inspired metaheuristic algorithm
2018
International Journal of Engineering & Technology
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In recent years, appreciable attention among analysts to take care of the extraordinary enhancement issues utilizing metaheuristic algorithms in the domain area of Swarm Intelligence. ...
Many metaheuristic algorithms have been developed by inspiring various nature phenomena's. ...
Survey on metaheuristic optimization algorithm Metaheuristic advancement manages streamlining issues utilizing metaheuristic calculations. ...
doi:10.14419/ijet.v7i3.18.14616
fatcat:b7vjxohdn5bvrlcpumqs56xnqm
Redistricting Algorithms
[article]
2020
arXiv
pre-print
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In this chapter, two computer scientists survey what's been done in algorithmic redistricting, discuss what doesn't work and highlight approaches that show promise. ...
But there are more than a couple problems with this idea. ...
Interestingly, the edges near the higher populated Iowa counties are also substantially more frequently cut than the less populated counties (see Figure 3). ...
arXiv:2011.09504v1
fatcat:5uxbxhvpuzf3vpcv7o3flo36zu
Comparative study of whale optimization algorithm and flower pollination algorithm to solve workers assignment problem
2022
International Journal of Production Management and Engineering
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Many important problems in engineering management can be formulated as Resource Assignment Problem (RAP). ...
Two metaheuristic optimizations named Whale Optimization Algorithm (WOA) and Flower Pollination Algorithm (FPA) are utilized to final the optimal solution that reduces the total cost. ...
On other words, different algorithms could be tested and evaluated with less time. As a consequence of using simulation, more knowledge and insight can be gained to enhance the solution of WAP. ...
doi:10.4995/ijpme.2022.16736
fatcat:gnuiir3glffr7gg4c4it6rn7em
Weevil damage optimization algorithm and its applications
2022
Journal of Future Sustainability
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This research proposes a novel swarm-based metaheuristics algorithm called Weevil Damage Optimization Algorithm (WDOA) which mimics weevils' fly power, snout power, and damage power on crops or agricultural ...
Additionally, the proposed WDOA is employed in five engineering problems to check its robustness for problem solving. ...
It has to be mentioned that HS and DE algorithms could solve the problem with less than 12 queens. ...
doi:10.5267/j.jfs.2022.10.003
fatcat:oeupdmvgendcbdeai234b2ukje
Ethane: A Heterogeneous Parallel Search Algorithm for Heterogeneous Platforms
[article]
2011
arXiv
pre-print
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The analysis will show that Ethane, though simple, can solve search problems in a faster and more robust way than well-known panmitic and distributed algorithms very popular in the literature. ...
We also propose a schema for describing a family of parallel heterogeneous metaheuristics inspired by the structure of hydrocarbons in Nature, HydroCM (HydroCarbon inspired Metaheuristics), establishing ...
It could be interesting for many tasks, but it does not offer the benefits of a structured population. ...
arXiv:1105.5900v2
fatcat:uxzc5mzegvh6pjo7xdyksdchre
Ebola Optimization Search Algorithm: A New Nature-Inspired Metaheuristic Optimization Algorithm
2022
IEEE Access
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The Ebola virus has a propagation strategy that allows individuals in a population to move among susceptible, infected, quarantined, hospitalized, recovered, and dead sub-population groups. ...
Extensive simulation results show that the EOSA outperforms popular metaheuristic algorithms such as the Particle Swarm Optimization Algorithm (PSO), Genetic Algorithm (GA), and Artificial Bee Colony Algorithm ...
In all cases, the CPU time for training EOSA was lower than other related algorithms. While EOSA showed less CPU time, GA and ABC were more demanding for this same computational resource. ...
doi:10.1109/access.2022.3147821
fatcat:lbb57sp75vah7aji52hylsr4uu
Combining heuristics and Exact Algorithms: A Review
[article]
2022
arXiv
pre-print
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These two have been established by different communities more or less in isolation from each other. ...
Mathematical programming techniques, including (integer) linear programming-based methods and metaheuristic approaches, are two highly successful streams for combinatorial problems. ...
More sophisticated metaheuristics have so far been used less frequently. ...
arXiv:2202.02799v1
fatcat:ciksnwc4qfdddk2ktflxxnqsse
Service oriented evolutionary algorithms
2013
Soft Computing - A Fusion of Foundations, Methodologies and Applications
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With this implementation, less effort than classical development in integration, distribution mechanisms and execution time management has been attained. ...
In addition, steps, ideas, advantages and disadvantages, and guidelines to create service oriented evolutionary algorithms are presented. ...
Acknowledgements This work has been supported in part by FPU research grant AP2009-2942 and projects AmIVital (CENIT2007-1010), EvOrq (TIC-3903), and TIN2011-28627-C04-02. ...
doi:10.1007/s00500-013-0999-5
fatcat:etf5szvwijgclbmhlvial5ug3e
Automated Design of Metaheuristic Algorithms: A Survey
[article]
2024
arXiv
pre-print
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This gives rise to increasing interest in automated design of metaheuristic algorithms. ...
Manually designing metaheuristic algorithms for solving a target problem is criticized for being laborious, error-prone, and requiring intensive specialized knowledge. ...
This enables building up an algorithm operator-by-operator or primitive-by-primitive, which could be expected to be more efficient than building the whole algorithm at once. ...
arXiv:2303.06532v3
fatcat:rznuaj5vj5c6jodxyxlnuequge
Differential Evolution Algorithm based Hyper-Parameters Selection of Transformer Neural Network Model for Load Forecasting
[article]
2024
arXiv
pre-print
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Our work compares the proposed Transformer based Neural Network model integrated with different metaheuristic algorithms by their performance in Load forecasting based on numerical metrics such as Mean ...
Our findings demonstrate the potential of metaheuristic-enhanced Transformer-based Neural Network models in Load forecasting accuracy and provide optimal hyperparameters for each model. ...
Due to possessing limited computational resources, each metaheuristic algorithm couldn't be applied to sufficiently large populations over many generations. ...
arXiv:2307.15299v5
fatcat:ft367iwabvc7rcgydp3r7y6pla
A Hybrid Algorithm for Metaheuristic Optimization
[article]
2019
arXiv
pre-print
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The information produced by each individual agent can be combinedin various ways via higher-level operators. ...
In our experiments on keybenchmark functions, we investigate how the performance of our algorithmvaries with respect to several of its key modifiable properties. ...
This indicates that MMO is more robust than SGD and is less likely to overfit during the training process. ...
arXiv:1906.02010v1
fatcat:7awolisr2zfwtnhok2nc4dhkme
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