Reliable Cluster Head(CH)selectionbased routing protocols are necessary for increasing the packet transmission efficiency with optimal path discovery that never introduces degradation over the transmission reliability...Reliable Cluster Head(CH)selectionbased routing protocols are necessary for increasing the packet transmission efficiency with optimal path discovery that never introduces degradation over the transmission reliability.In this paper,Hybrid Golden Jackal,and Improved Whale Optimization Algorithm(HGJIWOA)is proposed as an effective and optimal routing protocol that guarantees efficient routing of data packets in the established between the CHs and the movable sink.This HGJIWOA included the phases of Dynamic Lens-Imaging Learning Strategy and Novel Update Rules for determining the reliable route essential for data packets broadcasting attained through fitness measure estimation-based CH selection.The process of CH selection achieved using Golden Jackal Optimization Algorithm(GJOA)completely depends on the factors of maintainability,consistency,trust,delay,and energy.The adopted GJOA algorithm play a dominant role in determining the optimal path of routing depending on the parameter of reduced delay and minimal distance.It further utilized Improved Whale Optimisation Algorithm(IWOA)for forwarding the data from chosen CHs to the BS via optimized route depending on the parameters of energy and distance.It also included a reliable route maintenance process that aids in deciding the selected route through which data need to be transmitted or re-routed.The simulation outcomes of the proposed HGJIWOA mechanism with different sensor nodes confirmed an improved mean throughput of 18.21%,sustained residual energy of 19.64%with minimized end-to-end delay of 21.82%,better than the competitive CH selection approaches.展开更多
Dynamic Economic Emission Dispatch(DEED)aims to optimize control over fuel cost and pollution emission,two conflicting objectives,by scheduling the output power of various units at specific times.Although many methods...Dynamic Economic Emission Dispatch(DEED)aims to optimize control over fuel cost and pollution emission,two conflicting objectives,by scheduling the output power of various units at specific times.Although many methods well-performed on the DEED problem,most of them fail to achieve expected results in practice due to a lack of effective trade-off mechanisms between the convergence and diversity of non-dominated optimal dispatching solutions.To address this issue,a new multi-objective solver called Multi-Objective Golden Jackal Optimization(MOGJO)algorithm is proposed to cope with the DEED problem.The proposed algorithm first stores non-dominated optimal solutions found so far into an archive.Then,it chooses the best dispatching solution from the archive as the leader through a selection mechanism designed based on elite selection strategy and Euclidean distance index method.This mechanism can guide the algorithm to search for better dispatching solutions in the direction of reducing fuel costs and pollutant emissions.Moreover,the basic golden jackal optimization algorithm has the drawback of insufficient search,which hinders its ability to effectively discover more Pareto solutions.To this end,a non-linear control parameter based on the cosine function is introduced to enhance global exploration of the dispatching space,thus improving the efficiency of finding the optimal dispatching solutions.The proposed MOGJO is evaluated on the latest CEC benchmark test functions,and its superiority over the state-of-the-art multi-objective optimizers is highlighted by performance indicators.Also,empirical results on 5-unit,10-unit,IEEE 30-bus,and 30-unit systems show that the MOGJO can provide competitive compromise scheduling solutions compared to published DEED methods.Finally,in the analysis of the Pareto dominance relationship and the Euclidean distance index,the optimal dispatching solutions provided by MOGJO are the closest to the ideal solutions for minimizing fuel costs and pollution emissions simultaneously,compared to the latest published DEED solutions.展开更多
From the end of 2019 until now,the Coronavirus Disease 2019(COVID-19)has been rampaging around the world,posing a great threat to people's lives and health,as well as a serious impact on economic development.Consi...From the end of 2019 until now,the Coronavirus Disease 2019(COVID-19)has been rampaging around the world,posing a great threat to people's lives and health,as well as a serious impact on economic development.Considering the severely infectious nature of COVID-19,the diagnosis of COVID-19 has become crucial.Identification through the use of Computed Tomography(CT)images is an efficient and quick means.Therefore,scientific researchers have proposed numerous segmentation methods to improve the diagnosis of CT images.In this paper,we propose a reinforcement learning-based golden jackal optimization algorithm,which is named QLGJO,to segment CT images in furtherance of the diagnosis of COVID-19.Reinforcement learning is combined for the first time with meta-heuristics in segmentation problem.This strategy can effectively overcome the disadvantage that the original algorithm tends to fall into local optimum.In addition,one hybrid model and three different mutation strategies were applied to the update part of the algorithm in order to enrich the diversity of the population.Two experiments were carried out to test the performance of the proposed algorithm.First,compare QLGJO with other advanced meta-heuristics using the IEEE CEC2022 benchmark functions.Secondly,QLGJO was experimentally evaluated on CT images of COVID-19 using the Otsu method and compared with several well-known meta-heuristics.It is shown that QLGJO is very competitive in benchmark function and image segmentation experiments compared with other advanced meta-heuristics.Furthermore,the source code of the QLGJO is publicly available at https://github.com/Vang-z/QLGJO.展开更多
This is the first reported study in which various cytological and microbial components of the ear canal of wild jackals (Canis aureus) were examined and compared with those of domesticated dogs (C. domesticus). It is ...This is the first reported study in which various cytological and microbial components of the ear canal of wild jackals (Canis aureus) were examined and compared with those of domesticated dogs (C. domesticus). It is proposed that the differences between them might be attributable to domestication. The normal cytology of the jackals' ears includes cerumen, keratinous debris, coccoid bacteria and yeast-like organisms similar to domesticated dogs, but the frequencies of these findings differed significantly between the two species. In the jackals the incidences of ceruminous debris and yeasts were significantly lower (p p = 0.004 respectively), while keratinous debris and coccoid bacteria were significantly higher (p < 0.001). During domestication some changes have probably occurred in the dogs' lifestyle that predisposed them to the growth of yeasts in their ears but less to bacterial growth. It is possible that the higher numbers of bacteria might be a result of environmental contamination, because some of the jackals lived near urban centers and feed on garbage.展开更多
The Golden Jackal (Canis aureus Linnaeus, 1758), which belongs to the Canidae family, is an opportunist carnivore in the Gaza Strip (365 square kilometers). The current study aims at giving notes on the occurrence and...The Golden Jackal (Canis aureus Linnaeus, 1758), which belongs to the Canidae family, is an opportunist carnivore in the Gaza Strip (365 square kilometers). The current study aims at giving notes on the occurrence and some ecological aspects of the species in the Gaza Strip, Palestine. The study, which lasted 14 years (2007-2020), is descriptive and cumulative in its style. It was based on frequent field visits, direct observations and meetings and discussions with wildlife hunters, farmers and other stakeholders. The findings of the study show that Gazans are familiar with the Golden Jackal to the extent that a Gazan family holds the Arabic name of the animal, which is “Wawi”. The Golden Jackal was sometimes encountered and hunted in the eastern parts of the Gaza Strip, which are characterized by the presence of wilderness areas, intensive agriculture, poultry pens and solid waste landfills. Like other a few mammalian faunas, the adult Golden Jackals enter the Gaza Strip through gaps in or burrows beneath the metal borders separating the Gaza Strip from the rest of the Palestinian Territories and Egypt. Gaza zoos were found to harbor tens of Golden Jackals trapped or hunted by clever wildlife hunters using different means such as wire cages known locally as “maltash” and foothold traps with metal jaws that may cause lesions to the trapped animals. Poisoning and shooting were also common methods used to control the jackals and other carnivores causing harm to agriculture and livestock. The animal was known among the Gazans as an omnivore, feeding on wild and domestic animals in addition to plant materials, garbage and carrions. In conclusion, the study recommends the need to raise ecological awareness to preserve the Golden jackal and to adopt safe control measures for jackals and other carnivores, including the construction of protective fences for agricultural fields and animal pens.展开更多
This paper proposes a modified golden jackal optimization(IGJO)algorithm to solve the OCL(which stands for optimal cooling load)problem to minimize energy consumption.In this algorithm,many tools have been developed,s...This paper proposes a modified golden jackal optimization(IGJO)algorithm to solve the OCL(which stands for optimal cooling load)problem to minimize energy consumption.In this algorithm,many tools have been developed,such as numerical visualization,local field method,competitive selectionmethod,and iterative strategy.The IGJO algorithm is used to improve the research capabilities of the algorithm in terms of global tuning and rotation speed.In order to fully utilize the effectiveness of the proposed algorithm,three famous examples of OCL problems in basic ventilation systems were studied and compared with some previously published works.The results show that the IGJO algorithm can find solutions equal to or better than other methods.Underpinning these studies is the need to reduce energy consumption in air conditioning systems,which is a critical business and environmental decision.The Optimal Chiller Load(OCL)problem is well-known in the industry.It is the best method of operation for the refrigeration plant to satisfy the requirement of cooling.In order to solve the OCL problem,an improved Golden Jackal optimization algorithm(IGJO)was proposed.The IGJO algorithm consists of a number of parts to improve the global optimization and rotation speed.These studies are intended to address more effectively the issue of OCL,which results in energy savings in air-conditioning systems.The performance of the proposed IGJO algorithm is evaluated,and the results are compared with the results of three known OCL problems in the ventilation system.The results indicate that the IGJO method has the same or better optimization ability as other methods and can improve the energy efficiency of the system’s cold air.展开更多
This study aims to optimize an isolated solar-wind-diesel microgrid to reduce reliance on diesel generators,lower operational costs,and mitigate environmental pollution in remote areas.In this optimization,arithmetic ...This study aims to optimize an isolated solar-wind-diesel microgrid to reduce reliance on diesel generators,lower operational costs,and mitigate environmental pollution in remote areas.In this optimization,arithmetic opti-mization algorithm and golden jackal optimization are combined for achieving optimal capacity planning,considering economic and emission dispatch factors.This combination enhances the optimization by considering the balance in exploration and exploitation offered by the arithmetic operators of the arithmetic optimization algorithm and the dynamic adjustment by the adaptive search of the golden jackal optimization.Performance analysis is conducted by simulating and comparing three scenarios of only diesel generators,solar-wind-diesel and solar-wind with low number of diesel generators.The results demonstrate significant cost savings using the solar-wind-diesel microgrid under the proposed combined optimization compared to the arithmetic opti-mization algorithm and golden jackal algorithm and conventional metaheuristic optimization based on genetic algorithms.展开更多
Nowadays,optimization techniques are required in various engineering domains to find optimal solutions for complex problems.As a result,there is a growing tendency among scientists to enhance existing nature-inspired ...Nowadays,optimization techniques are required in various engineering domains to find optimal solutions for complex problems.As a result,there is a growing tendency among scientists to enhance existing nature-inspired algorithms using various evolutionary strategies and to develop new nature-inspired optimization methods that can properly explore the feature space.The recently designed nature-inspired meta-heuristic,named the Golden Jackal Optimization(GJO),was inspired by the collaborative hunting actions of the golden jackal in nature to solve various challenging problems.However,like other approaches,the GJO has the limitations of poor exploitation ability,the ease of getting stuck in a local optimal region,and an improper balancing of exploration and exploitation.To overcome these limitations,this paper proposes an improved GJO algorithm based on multi-strategy mixing(LGJO).First,using a chaotic mapping strategy to initialize the population instead of using random parameters,this algorithm can generate initial solutions with good diversity in the search space.Second,a dynamic inertia weight based on cosine variation is proposed to make the search process more realistic and effectively balance the algorithm's global and local search capabilities.Finally,a position update strategy based on Gaussian mutation was introduced,fully utilizing the guidance role of the optimal individual to improve population diversity,effectively exploring unknown regions,and avoiding the algorithm falling into local optima.To evaluate the proposed algorithm,23 mathematical benchmark functions,CEC-2019 and CEC2021 tests are employed.The results are compared to high-quality,well-known optimization methods.The results of the proposed method are compared from different points of view,including the quality of the results,convergence behavior,and robustness.The superiority and high-quality performance of the proposed method are demonstrated by comparing the results.Furthermore,to demonstrate its applicability,it is employed to solve four constrained industrial applications.The outcomes of the experiment reveal that the proposed algorithm can solve challenging,constrained problems and is very competitive compared with other optimization algorithms.This article provides a new approach to solving real-world optimization problems.展开更多
文摘Reliable Cluster Head(CH)selectionbased routing protocols are necessary for increasing the packet transmission efficiency with optimal path discovery that never introduces degradation over the transmission reliability.In this paper,Hybrid Golden Jackal,and Improved Whale Optimization Algorithm(HGJIWOA)is proposed as an effective and optimal routing protocol that guarantees efficient routing of data packets in the established between the CHs and the movable sink.This HGJIWOA included the phases of Dynamic Lens-Imaging Learning Strategy and Novel Update Rules for determining the reliable route essential for data packets broadcasting attained through fitness measure estimation-based CH selection.The process of CH selection achieved using Golden Jackal Optimization Algorithm(GJOA)completely depends on the factors of maintainability,consistency,trust,delay,and energy.The adopted GJOA algorithm play a dominant role in determining the optimal path of routing depending on the parameter of reduced delay and minimal distance.It further utilized Improved Whale Optimisation Algorithm(IWOA)for forwarding the data from chosen CHs to the BS via optimized route depending on the parameters of energy and distance.It also included a reliable route maintenance process that aids in deciding the selected route through which data need to be transmitted or re-routed.The simulation outcomes of the proposed HGJIWOA mechanism with different sensor nodes confirmed an improved mean throughput of 18.21%,sustained residual energy of 19.64%with minimized end-to-end delay of 21.82%,better than the competitive CH selection approaches.
基金supported by the National Natural Science Foundation of China under Grant No.61802328,61972333,and 61771415.
文摘Dynamic Economic Emission Dispatch(DEED)aims to optimize control over fuel cost and pollution emission,two conflicting objectives,by scheduling the output power of various units at specific times.Although many methods well-performed on the DEED problem,most of them fail to achieve expected results in practice due to a lack of effective trade-off mechanisms between the convergence and diversity of non-dominated optimal dispatching solutions.To address this issue,a new multi-objective solver called Multi-Objective Golden Jackal Optimization(MOGJO)algorithm is proposed to cope with the DEED problem.The proposed algorithm first stores non-dominated optimal solutions found so far into an archive.Then,it chooses the best dispatching solution from the archive as the leader through a selection mechanism designed based on elite selection strategy and Euclidean distance index method.This mechanism can guide the algorithm to search for better dispatching solutions in the direction of reducing fuel costs and pollutant emissions.Moreover,the basic golden jackal optimization algorithm has the drawback of insufficient search,which hinders its ability to effectively discover more Pareto solutions.To this end,a non-linear control parameter based on the cosine function is introduced to enhance global exploration of the dispatching space,thus improving the efficiency of finding the optimal dispatching solutions.The proposed MOGJO is evaluated on the latest CEC benchmark test functions,and its superiority over the state-of-the-art multi-objective optimizers is highlighted by performance indicators.Also,empirical results on 5-unit,10-unit,IEEE 30-bus,and 30-unit systems show that the MOGJO can provide competitive compromise scheduling solutions compared to published DEED methods.Finally,in the analysis of the Pareto dominance relationship and the Euclidean distance index,the optimal dispatching solutions provided by MOGJO are the closest to the ideal solutions for minimizing fuel costs and pollution emissions simultaneously,compared to the latest published DEED solutions.
基金supported by the National Natural Science Foundation of China[grant numbers 21466008]the Guangxi Natural Science Foundation,China[grant numbers 2019GXNSFAA185017]+1 种基金the Scientific Research Project of Guangxi Minzu University[grant numbers 2021MDKJ004]the Innovation Project of Guangxi Graduate Education[grant numbers YCSW2022255].
文摘From the end of 2019 until now,the Coronavirus Disease 2019(COVID-19)has been rampaging around the world,posing a great threat to people's lives and health,as well as a serious impact on economic development.Considering the severely infectious nature of COVID-19,the diagnosis of COVID-19 has become crucial.Identification through the use of Computed Tomography(CT)images is an efficient and quick means.Therefore,scientific researchers have proposed numerous segmentation methods to improve the diagnosis of CT images.In this paper,we propose a reinforcement learning-based golden jackal optimization algorithm,which is named QLGJO,to segment CT images in furtherance of the diagnosis of COVID-19.Reinforcement learning is combined for the first time with meta-heuristics in segmentation problem.This strategy can effectively overcome the disadvantage that the original algorithm tends to fall into local optimum.In addition,one hybrid model and three different mutation strategies were applied to the update part of the algorithm in order to enrich the diversity of the population.Two experiments were carried out to test the performance of the proposed algorithm.First,compare QLGJO with other advanced meta-heuristics using the IEEE CEC2022 benchmark functions.Secondly,QLGJO was experimentally evaluated on CT images of COVID-19 using the Otsu method and compared with several well-known meta-heuristics.It is shown that QLGJO is very competitive in benchmark function and image segmentation experiments compared with other advanced meta-heuristics.Furthermore,the source code of the QLGJO is publicly available at https://github.com/Vang-z/QLGJO.
文摘This is the first reported study in which various cytological and microbial components of the ear canal of wild jackals (Canis aureus) were examined and compared with those of domesticated dogs (C. domesticus). It is proposed that the differences between them might be attributable to domestication. The normal cytology of the jackals' ears includes cerumen, keratinous debris, coccoid bacteria and yeast-like organisms similar to domesticated dogs, but the frequencies of these findings differed significantly between the two species. In the jackals the incidences of ceruminous debris and yeasts were significantly lower (p p = 0.004 respectively), while keratinous debris and coccoid bacteria were significantly higher (p < 0.001). During domestication some changes have probably occurred in the dogs' lifestyle that predisposed them to the growth of yeasts in their ears but less to bacterial growth. It is possible that the higher numbers of bacteria might be a result of environmental contamination, because some of the jackals lived near urban centers and feed on garbage.
文摘The Golden Jackal (Canis aureus Linnaeus, 1758), which belongs to the Canidae family, is an opportunist carnivore in the Gaza Strip (365 square kilometers). The current study aims at giving notes on the occurrence and some ecological aspects of the species in the Gaza Strip, Palestine. The study, which lasted 14 years (2007-2020), is descriptive and cumulative in its style. It was based on frequent field visits, direct observations and meetings and discussions with wildlife hunters, farmers and other stakeholders. The findings of the study show that Gazans are familiar with the Golden Jackal to the extent that a Gazan family holds the Arabic name of the animal, which is “Wawi”. The Golden Jackal was sometimes encountered and hunted in the eastern parts of the Gaza Strip, which are characterized by the presence of wilderness areas, intensive agriculture, poultry pens and solid waste landfills. Like other a few mammalian faunas, the adult Golden Jackals enter the Gaza Strip through gaps in or burrows beneath the metal borders separating the Gaza Strip from the rest of the Palestinian Territories and Egypt. Gaza zoos were found to harbor tens of Golden Jackals trapped or hunted by clever wildlife hunters using different means such as wire cages known locally as “maltash” and foothold traps with metal jaws that may cause lesions to the trapped animals. Poisoning and shooting were also common methods used to control the jackals and other carnivores causing harm to agriculture and livestock. The animal was known among the Gazans as an omnivore, feeding on wild and domestic animals in addition to plant materials, garbage and carrions. In conclusion, the study recommends the need to raise ecological awareness to preserve the Golden jackal and to adopt safe control measures for jackals and other carnivores, including the construction of protective fences for agricultural fields and animal pens.
文摘This paper proposes a modified golden jackal optimization(IGJO)algorithm to solve the OCL(which stands for optimal cooling load)problem to minimize energy consumption.In this algorithm,many tools have been developed,such as numerical visualization,local field method,competitive selectionmethod,and iterative strategy.The IGJO algorithm is used to improve the research capabilities of the algorithm in terms of global tuning and rotation speed.In order to fully utilize the effectiveness of the proposed algorithm,three famous examples of OCL problems in basic ventilation systems were studied and compared with some previously published works.The results show that the IGJO algorithm can find solutions equal to or better than other methods.Underpinning these studies is the need to reduce energy consumption in air conditioning systems,which is a critical business and environmental decision.The Optimal Chiller Load(OCL)problem is well-known in the industry.It is the best method of operation for the refrigeration plant to satisfy the requirement of cooling.In order to solve the OCL problem,an improved Golden Jackal optimization algorithm(IGJO)was proposed.The IGJO algorithm consists of a number of parts to improve the global optimization and rotation speed.These studies are intended to address more effectively the issue of OCL,which results in energy savings in air-conditioning systems.The performance of the proposed IGJO algorithm is evaluated,and the results are compared with the results of three known OCL problems in the ventilation system.The results indicate that the IGJO method has the same or better optimization ability as other methods and can improve the energy efficiency of the system’s cold air.
基金supported in part by Canadian Research Knowledge Network(CRKN),Natural Sciences and Engineering Research Council of Canada(NSERC),Discovery Grants RGPIN-2024-04568Khalifa Uni-versity of Science and Technology(KUST),Abu Dhabi,UAE,under Award CIRA-2021-063 and ASPIRE VRI-Sustainability.
文摘This study aims to optimize an isolated solar-wind-diesel microgrid to reduce reliance on diesel generators,lower operational costs,and mitigate environmental pollution in remote areas.In this optimization,arithmetic opti-mization algorithm and golden jackal optimization are combined for achieving optimal capacity planning,considering economic and emission dispatch factors.This combination enhances the optimization by considering the balance in exploration and exploitation offered by the arithmetic operators of the arithmetic optimization algorithm and the dynamic adjustment by the adaptive search of the golden jackal optimization.Performance analysis is conducted by simulating and comparing three scenarios of only diesel generators,solar-wind-diesel and solar-wind with low number of diesel generators.The results demonstrate significant cost savings using the solar-wind-diesel microgrid under the proposed combined optimization compared to the arithmetic opti-mization algorithm and golden jackal algorithm and conventional metaheuristic optimization based on genetic algorithms.
基金support of the special project for collaborative innovation of science and technology in 2021(No:202121206)Henan Province University Scientific and Technological Innovation Team(No:18IRTSTHN009).
文摘Nowadays,optimization techniques are required in various engineering domains to find optimal solutions for complex problems.As a result,there is a growing tendency among scientists to enhance existing nature-inspired algorithms using various evolutionary strategies and to develop new nature-inspired optimization methods that can properly explore the feature space.The recently designed nature-inspired meta-heuristic,named the Golden Jackal Optimization(GJO),was inspired by the collaborative hunting actions of the golden jackal in nature to solve various challenging problems.However,like other approaches,the GJO has the limitations of poor exploitation ability,the ease of getting stuck in a local optimal region,and an improper balancing of exploration and exploitation.To overcome these limitations,this paper proposes an improved GJO algorithm based on multi-strategy mixing(LGJO).First,using a chaotic mapping strategy to initialize the population instead of using random parameters,this algorithm can generate initial solutions with good diversity in the search space.Second,a dynamic inertia weight based on cosine variation is proposed to make the search process more realistic and effectively balance the algorithm's global and local search capabilities.Finally,a position update strategy based on Gaussian mutation was introduced,fully utilizing the guidance role of the optimal individual to improve population diversity,effectively exploring unknown regions,and avoiding the algorithm falling into local optima.To evaluate the proposed algorithm,23 mathematical benchmark functions,CEC-2019 and CEC2021 tests are employed.The results are compared to high-quality,well-known optimization methods.The results of the proposed method are compared from different points of view,including the quality of the results,convergence behavior,and robustness.The superiority and high-quality performance of the proposed method are demonstrated by comparing the results.Furthermore,to demonstrate its applicability,it is employed to solve four constrained industrial applications.The outcomes of the experiment reveal that the proposed algorithm can solve challenging,constrained problems and is very competitive compared with other optimization algorithms.This article provides a new approach to solving real-world optimization problems.
