Chaotic grasshopper optimization algorithm for global optimization

Arora, Sankalap; Anand, Priyanka

doi:10.1007/s00521-018-3343-2

Cited by 252 publications

(100 citation statements)

References 36 publications

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“…Composite functions (F14‐F23) have lower dimensions and fewer local minima than the previous groups. They are the combination of different rotated and shifted biased multimodal test functions …”

Section: Computational Resultsmentioning

confidence: 99%

“…They are the combination of different rotated and shifted biased multimodal test functions. 6 To verify the capability and competitiveness of CMVO to solve different global test functions, it is compared with seven popular algorithms, namely, MVO that is the original version of CMVO, PSO that is the best algorithm among swarm-based techniques, GA that is the best among evolutionary algorithms, and GOA, LSO, SSA, WOA that are the latest nature-inspired metaheuristic algorithms presented in the literature.…”

Section: Results Of Cmvo Algorithmmentioning

confidence: 99%

“…Metaheuristic algorithms are regrouped by the number of individuals used at the same time. Metaheuristic algorithms have two main classes: individual and population based . In the individual‐based class, a single particle is produced for a specific problem and is improved during the iterative process (eg, trajectory methods) .…”

Section: Introductionmentioning

confidence: 99%

“…Metaheuristic algorithms have two main classes: individual and population based. 6 In the individual-based class, a single particle is produced for a specific problem and is improved during the iterative process (eg, trajectory methods). 7 By contrast, the population-based class performs a search process to solve a problem by describing candidate solutions with parallel computing.…”

Section: Introductionmentioning

confidence: 99%

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A new competitive multiverse optimization technique for solving single‐objective and multiobjective problems

Benmessahel

Xie

Chellal

2020

Engineering Reports

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The development of useful algorithms for solving global optimization problems has recently drawing the research community's attention. A number of optimization algorithms have been suggested, which mimic a particular biological process or imitate natural evolution. In this work, a novel population‐based optimization technique is proposed, the so‐called competitive multiverse optimizer (CMVO) for solving global optimization problems. This novel method is fundamentally inspired by the multiverse optimizer algorithm (MVO) but with a different framework. Our basic idea is to inject a pairwise competition mechanism between universes and adopts a novel update strategy that make universes learn from the winner. In CMVO, the mechanism of updating universes is not confined to the optimal universe but rather uses a bicompetitive scheme at each generation in which the universe that loses the competition learns from the winner, unlike in MVO in which all universes learn from the optimal universe. The main idea of this work is to raise the exploration rate for the search space by performing pairwise competition and improve the exploitation ability through learning from the winner. This work also presents a multi‐objective version of CMVO called MOCMVO with a simple structure to show the rapid convergence toward the Pareto front. The performance of our proposed method in single‐objective optimization is demonstrated on a large number of mathematical problems of standard benchmark. The results obtained confirm the superior overall performance of CMVO in terms of the quality of obtained solutions, computational efficiency, and convergence speed relative to many of the state‐of‐the‐art metaheuristic algorithms. In multiobjective optimization, the proposed MOCMVO is evaluated and compared to other multiobjective algorithms using 10 multiobjective benchmarks involving five unconstrained, three constrained, and two engineering design problems. The experimental results of the proposed method in multiobjective optimization demonstrate the competitiveness of the proposed method in term of quantitative and qualitative measures.

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Section: Computational Resultsmentioning

confidence: 99%

Section: Results Of Cmvo Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A new competitive multiverse optimization technique for solving single‐objective and multiobjective problems

Benmessahel

Xie

Chellal

2020

Engineering Reports

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“…50 In this research, the sinusoidal chaotic map is utilized for improving the WCO algorithm. [51][52][53][54] In chaos theory, all the complicated systems have a deterministic formulation and they just seem as random values. The sinusoidal chaotic map is a chaotic term that is employed in different applications.…”

Section: Improved World Cup Optimization Algorithm Based On Chaos Thementioning

confidence: 99%

Using chaos world cup optimization algorithm for medical images contrast enhancement

Wang

Liu

Jimenez

2019

Concurrency and Computation

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In this paper, a chaos-based optimization algorithm is employed for medical images contrast enhancement. Here, a weighted combined framework is suggested as the cost function for the contrast enhancement. The method utilizes the advantages of Gamma correction, histogram equalization, and edge information for decreasing the original image features losses. For more enhancements, a piecewise version of Gamma correction is also utilized to decrease the unnatural artifacts in the output image. A combined cost function is employed based on the three aforementioned features and the proposed chaos world cup optimization algorithm is used for maximizing the fitness function. The simulation results have been compared with five state-of-the-art methods for presenting the method efficiency. To do this, contrast, homogeneity, weighted average peak SNR, a measure of enhancement, and contrast noise ratio are employed. The results also applied to two standard medical imaging datasets and compared with the other methods. Final results denoted that the presented multiobjective optimization algorithm improves the quality of the image contrast and can illustrate more details and information toward the other compared methods.

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Evaluating climate changes and land use changes on water resources using hybrid Soil and Water Assessment Tool‐DEEP optimized by metaheuristics

Liu

Mizzi

2020

Concurrency and Computation

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The objective of this research is to investigate the effect of land-use and climate change on the volume of runoff produced and its impact on water resources. It is clear that the more accurate the assessment of runoff in the future, the better the evaluation of the impact of land-use and climate changes on water resources. Thus, to more accurately predict water sources in the future, runoff is estimated using two Soil and Water Assessment Tool (SWAT) and SWAT-DEEP-IFSO models. The results showed that the SWAT-DEEP-IFSO model has a better estimation than the SWAT method. Because Deep Belief Network and an improved optimization algorithm called Improved Fluid Search Optimization (IFSO) algorithm have been utilized in optimizing the hybrid SWAT model that can reduce the error of runoff simulation. After selecting the best model for estimating runoff, the effect of land-use and climate change is evaluated using different scenarios. Results show that the simulated runoff under the influence of climate and land-use changes can impact on the average yearly and monthly runoff. These factors have a significant effect on the average yearly runoff changes and are predicted an increasing trend for average yearly runoff. But climate and land-use changes have a different impact on the different months and for some months of the year the runoff has an increasing trend and for some months the runoff has a decreasing trend. Based on land-use scenarios, it is found that due to the decrease in rangelands and increase in agricultural lands, there is a possibility of severe floods and drought periods in the region, which reduces the water resources of the study area. K E Y W O R D S deep belief network, fluid search optimization algorithm, runoff, soil and water assessment tool, water resource 1 INTRODUCTION Today, all climate scientists agree that global warming causes important climatic phenomena and due to climate changes, the probability of heavy rainfall and flood risk has been increased. 1 Global warming dramatically affects rainfall characteristics, soil moisture, water levels of the seas and oceans, and airflow patterns. Also, it can cause severe floods and storms and reduce glaciers. Reducing snowfall and rain and increasing drought, thereby increasing soil salinity and reducing freshwater resources in coastal areas can be another result of global warming. 2 One of the effects of climate change can be seen in renewables resources such as water resources which have different impacts from point-to-point. This effect of

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Chaotic grasshopper optimization algorithm for global optimization

Cited by 252 publications

References 36 publications

A new competitive multiverse optimization technique for solving single‐objective and multiobjective problems

A new competitive multiverse optimization technique for solving single‐objective and multiobjective problems

Using chaos world cup optimization algorithm for medical images contrast enhancement

Evaluating climate changes and land use changes on water resources using hybrid Soil and Water Assessment Tool‐DEEP optimized by metaheuristics

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