In order to improve the global search ability of biogeography-based optimization(BBO)algorithm in multi-threshold image segmentation,a multi-threshold image segmentation based on improved BBO algorithm is proposed.Whe...In order to improve the global search ability of biogeography-based optimization(BBO)algorithm in multi-threshold image segmentation,a multi-threshold image segmentation based on improved BBO algorithm is proposed.When using BBO algorithm to optimize threshold,firstly,the elitist selection operator is used to retain the optimal set of solutions.Secondly,a migration strategy based on fusion of good solution and pending solution is introduced to reduce premature convergence and invalid migration of traditional migration operations.Thirdly,to reduce the blindness of traditional mutation operations,a mutation operation through binary computation is created.Then,it is applied to the multi-threshold image segmentation of two-dimensional cross entropy.Finally,this method is used to segment the typical image and compared with two-dimensional multi-threshold segmentation based on particle swarm optimization algorithm and the two-dimensional multi-threshold image segmentation based on standard BBO algorithm.The experimental results show that the method has good convergence stability,it can effectively shorten the time of iteration,and the optimization performance is better than the standard BBO algorithm.展开更多
The direct torque control of the dual star induction motor(DTC-DSIM) using conventional PI controllers is characterized by unsatisfactory performance, such as high ripples of torque and flux, and sensitivity to parame...The direct torque control of the dual star induction motor(DTC-DSIM) using conventional PI controllers is characterized by unsatisfactory performance, such as high ripples of torque and flux, and sensitivity to parametric variations. Among the most evoked control strategies adopted in this field to overcome these drawbacks presented in classical drive, it is worth mentioning the use of the second order sliding mode control(SOSMC) based on the super twisting algorithm(STA) combined with the fuzzy logic control(FSOSMC). In order to realize the optimal control performance, the FSOSMC parameters are adjusted using an optimization algorithm based on the genetic algorithm(GA). The performances of the envisaged control scheme, called G-FSOSMC, are investigated against G-SOSMC, G-PI and BBO-FSOSMC algorithms. The proposed controller scheme is efficient in reducing the torque and flux ripples, and successfully suppresses chattering. The effects of parametric uncertainties do not affect system performance.展开更多
Currently, biodiesel is presented as one of the best alternatives for gradually replacing the use of fossil fuels, but it has some factors that make it economically impractical if it does not have a government support...Currently, biodiesel is presented as one of the best alternatives for gradually replacing the use of fossil fuels, but it has some factors that make it economically impractical if it does not have a government support. For this reason, research efforts focused on this area have been responsible for optimizing the process of biodiesel production by different catalytic routes to achieve greater efficiency at a lower cost. In this case, the biggest problem has been the high cost generated by an investigation, which in many occasions is the main factor to decide if an investigation could be carried out. Trying to reduce these costs, in the current study, we are using a technique of glycerol quantification by volumetric methods and comparing obtained results with the chromatographic method, which is conventionally used and comparatively much more expensive. Biodiesel employee was obtained by an enzymatic catalysis process varying one of three process variables:oil:alcohol molar ratio, temperature and proportion of catalyst. The numerical differences obtained between the two quantification methods generated relative errors lower than 10%, resulting in some occasions lower than 1%. By gas chromatography analysis the best yield was obtained at the same conditions of the volumetric method, a temperature of 45 ℃, an oil:alcohol ratio 1:4 and 8 wt.% of catalyst, but a yield of 95.5% and 97.1%, respectively. Due to the high precision of gas chromatography, this method is used to carry out a surface response analysis obtaining as ideal operating conditions a temperature of 43.5 ℃, 8.9 wt.%. of catalyst and an oil:alcohol ratio 1:4.展开更多
Jatropha curcas L. (JCL) seeds were extracted and transesterified in-situ using supercritical methanol extraction in the absence of catalyst at different temperatures (200-280℃) and pressures (8-12 MPa), and at...Jatropha curcas L. (JCL) seeds were extracted and transesterified in-situ using supercritical methanol extraction in the absence of catalyst at different temperatures (200-280℃) and pressures (8-12 MPa), and at a fixed reaction time of 30 min with seeds-to-methanol ratio of 1:40 w/v. Design of experiment approach using five-level-two-factors design of Response Surface Methodology (RSM) was used to observe the effect of two independent variables i.e. temperature and pressure and the percent of biodiesel yield which required 13 runs. For optimization of the variables, Central Composite Rotatable Design (CCRD) was used for regression analysis and analysis of variance (ANOVA). The optimize conditions suggested by RSM were at T = 280℃ and P = 12.04 MPa. The predicted and experimental biodicsel yields were found to be 56.8% and 59.9%, respectively, with relatively small deviation errors of 1.59%.展开更多
Biologic behaviors are the principal source for proposing new intelligent algorithms. Based on the mechanism of the bio-subsistence and the bio-migration, this paper proposes a novel algorithm—Living Migration Algori...Biologic behaviors are the principal source for proposing new intelligent algorithms. Based on the mechanism of the bio-subsistence and the bio-migration, this paper proposes a novel algorithm—Living Migration Algorithm (LMA). The original contributions of LMA are three essential attributes of each individual: the minimal life-needs which are the necessaries for survival, the migrating which is a basal action for searching new living space, and the judging which is an important ability of deciding whether to migrate or not. When living space of all individuals can satisfy the minimal life-needs at some generation, they are considered as the optimal living places where objective functions will obtain the optima. LMA may be employed in large-scale computation and engineering field. The paper mostly operates LMA to deal with four non-linear and heterogeneous optimizations, and experiments prove LMA has better performances than Free Search algorithm.展开更多
The simple spectrophotometric method for the determination of esters has been developed. The procedure is based on the quantitative determination of ferric hydroxamate generated from the reaction of esters, hydroxylam...The simple spectrophotometric method for the determination of esters has been developed. The procedure is based on the quantitative determination of ferric hydroxamate generated from the reaction of esters, hydroxylamine hydrochloride in basic solution at 120℃, and ferric chloride. The formed complex was monitored through absorbance at 517 nm. The parameters affecting the absorbance value were explored for optimum analytical performance. The optimum conditions were 1.50 mL of 0.5 M hydroxylamine hydrochloride, 0.40 mL of 6 M sodium hydroxide, 1.00 mL of 3 M hydrochloric acid and 0.50 mL of 5 % ferric chloride. The method exhibits linear response up to 7.50% wt. and a detection limit of 0.23% wt. The linear range and working range were used at the same range 1.25 to 7.50% wt. The proposed method can be applied to biodiesel (B 100) for measuring the esters quantity in the percentage unit of biodiesel oil.展开更多
By adopting the distributed problem-solving strategy, swarm intelligence algorithms have been successfully applied to many optimization problems that are difficult to deal with using traditional methods. At present, t...By adopting the distributed problem-solving strategy, swarm intelligence algorithms have been successfully applied to many optimization problems that are difficult to deal with using traditional methods. At present, there are many well-implemented algorithms, such as particle swarm optimization, genetic algorithm, artificial bee colony algorithm, and ant colony optimization. These algorithms have already shown favorable performances. However, with the objects becoming increasingly complex, it is becoming gradually more difficult for these algorithms to meet human's demand in terms of accuracy and time. Designing a new algorithm to seek better solutions for optimization problems is becoming increasingly essential. Dolphins have many noteworthy biological characteristics and living habits such as echolocation, information exchanges, cooperation, and division of labor. Combining these biological characteristics and living habits with swarm intelligence and bringing them into optimization problems, we propose a brand new algorithm named the ‘dolphin swarm algorithm' in this paper. We also provide the definitions of the algorithm and specific descriptions of the four pivotal phases in the algorithm, which are the search phase, call phase, reception phase, and predation phase. Ten benchmark functions with different properties are tested using the dolphin swarm algorithm, particle swarm optimization, genetic algorithm, and artificial bee colony algorithm. The convergence rates and benchmark function results of these four algorithms are compared to testify the effect of the dolphin swarm algorithm. The results show that in most cases, the dolphin swarm algorithm performs better. The dolphin swarm algorithm possesses some great features, such as first-slow-then-fast convergence, periodic convergence, local-optimum-free, and no specific demand on benchmark functions. Moreover, the dolphin swarm algorithm is particularly appropriate to optimization problems, with more calls of fitness functions and fewer individuals.展开更多
基金Science and Technology Plan of Gansu Province(No.144NKCA040)
文摘In order to improve the global search ability of biogeography-based optimization(BBO)algorithm in multi-threshold image segmentation,a multi-threshold image segmentation based on improved BBO algorithm is proposed.When using BBO algorithm to optimize threshold,firstly,the elitist selection operator is used to retain the optimal set of solutions.Secondly,a migration strategy based on fusion of good solution and pending solution is introduced to reduce premature convergence and invalid migration of traditional migration operations.Thirdly,to reduce the blindness of traditional mutation operations,a mutation operation through binary computation is created.Then,it is applied to the multi-threshold image segmentation of two-dimensional cross entropy.Finally,this method is used to segment the typical image and compared with two-dimensional multi-threshold segmentation based on particle swarm optimization algorithm and the two-dimensional multi-threshold image segmentation based on standard BBO algorithm.The experimental results show that the method has good convergence stability,it can effectively shorten the time of iteration,and the optimization performance is better than the standard BBO algorithm.
基金Project supported by the LEB Research LaboratoryDepartment of Electrical Engineering,University of Batna 2, Algeria。
文摘The direct torque control of the dual star induction motor(DTC-DSIM) using conventional PI controllers is characterized by unsatisfactory performance, such as high ripples of torque and flux, and sensitivity to parametric variations. Among the most evoked control strategies adopted in this field to overcome these drawbacks presented in classical drive, it is worth mentioning the use of the second order sliding mode control(SOSMC) based on the super twisting algorithm(STA) combined with the fuzzy logic control(FSOSMC). In order to realize the optimal control performance, the FSOSMC parameters are adjusted using an optimization algorithm based on the genetic algorithm(GA). The performances of the envisaged control scheme, called G-FSOSMC, are investigated against G-SOSMC, G-PI and BBO-FSOSMC algorithms. The proposed controller scheme is efficient in reducing the torque and flux ripples, and successfully suppresses chattering. The effects of parametric uncertainties do not affect system performance.
文摘Currently, biodiesel is presented as one of the best alternatives for gradually replacing the use of fossil fuels, but it has some factors that make it economically impractical if it does not have a government support. For this reason, research efforts focused on this area have been responsible for optimizing the process of biodiesel production by different catalytic routes to achieve greater efficiency at a lower cost. In this case, the biggest problem has been the high cost generated by an investigation, which in many occasions is the main factor to decide if an investigation could be carried out. Trying to reduce these costs, in the current study, we are using a technique of glycerol quantification by volumetric methods and comparing obtained results with the chromatographic method, which is conventionally used and comparatively much more expensive. Biodiesel employee was obtained by an enzymatic catalysis process varying one of three process variables:oil:alcohol molar ratio, temperature and proportion of catalyst. The numerical differences obtained between the two quantification methods generated relative errors lower than 10%, resulting in some occasions lower than 1%. By gas chromatography analysis the best yield was obtained at the same conditions of the volumetric method, a temperature of 45 ℃, an oil:alcohol ratio 1:4 and 8 wt.% of catalyst, but a yield of 95.5% and 97.1%, respectively. Due to the high precision of gas chromatography, this method is used to carry out a surface response analysis obtaining as ideal operating conditions a temperature of 43.5 ℃, 8.9 wt.%. of catalyst and an oil:alcohol ratio 1:4.
文摘Jatropha curcas L. (JCL) seeds were extracted and transesterified in-situ using supercritical methanol extraction in the absence of catalyst at different temperatures (200-280℃) and pressures (8-12 MPa), and at a fixed reaction time of 30 min with seeds-to-methanol ratio of 1:40 w/v. Design of experiment approach using five-level-two-factors design of Response Surface Methodology (RSM) was used to observe the effect of two independent variables i.e. temperature and pressure and the percent of biodiesel yield which required 13 runs. For optimization of the variables, Central Composite Rotatable Design (CCRD) was used for regression analysis and analysis of variance (ANOVA). The optimize conditions suggested by RSM were at T = 280℃ and P = 12.04 MPa. The predicted and experimental biodicsel yields were found to be 56.8% and 59.9%, respectively, with relatively small deviation errors of 1.59%.
文摘Biologic behaviors are the principal source for proposing new intelligent algorithms. Based on the mechanism of the bio-subsistence and the bio-migration, this paper proposes a novel algorithm—Living Migration Algorithm (LMA). The original contributions of LMA are three essential attributes of each individual: the minimal life-needs which are the necessaries for survival, the migrating which is a basal action for searching new living space, and the judging which is an important ability of deciding whether to migrate or not. When living space of all individuals can satisfy the minimal life-needs at some generation, they are considered as the optimal living places where objective functions will obtain the optima. LMA may be employed in large-scale computation and engineering field. The paper mostly operates LMA to deal with four non-linear and heterogeneous optimizations, and experiments prove LMA has better performances than Free Search algorithm.
文摘The simple spectrophotometric method for the determination of esters has been developed. The procedure is based on the quantitative determination of ferric hydroxamate generated from the reaction of esters, hydroxylamine hydrochloride in basic solution at 120℃, and ferric chloride. The formed complex was monitored through absorbance at 517 nm. The parameters affecting the absorbance value were explored for optimum analytical performance. The optimum conditions were 1.50 mL of 0.5 M hydroxylamine hydrochloride, 0.40 mL of 6 M sodium hydroxide, 1.00 mL of 3 M hydrochloric acid and 0.50 mL of 5 % ferric chloride. The method exhibits linear response up to 7.50% wt. and a detection limit of 0.23% wt. The linear range and working range were used at the same range 1.25 to 7.50% wt. The proposed method can be applied to biodiesel (B 100) for measuring the esters quantity in the percentage unit of biodiesel oil.
基金Project supported by the National Key Technology R&D Program of China(No.2014BAD10B02)
文摘By adopting the distributed problem-solving strategy, swarm intelligence algorithms have been successfully applied to many optimization problems that are difficult to deal with using traditional methods. At present, there are many well-implemented algorithms, such as particle swarm optimization, genetic algorithm, artificial bee colony algorithm, and ant colony optimization. These algorithms have already shown favorable performances. However, with the objects becoming increasingly complex, it is becoming gradually more difficult for these algorithms to meet human's demand in terms of accuracy and time. Designing a new algorithm to seek better solutions for optimization problems is becoming increasingly essential. Dolphins have many noteworthy biological characteristics and living habits such as echolocation, information exchanges, cooperation, and division of labor. Combining these biological characteristics and living habits with swarm intelligence and bringing them into optimization problems, we propose a brand new algorithm named the ‘dolphin swarm algorithm' in this paper. We also provide the definitions of the algorithm and specific descriptions of the four pivotal phases in the algorithm, which are the search phase, call phase, reception phase, and predation phase. Ten benchmark functions with different properties are tested using the dolphin swarm algorithm, particle swarm optimization, genetic algorithm, and artificial bee colony algorithm. The convergence rates and benchmark function results of these four algorithms are compared to testify the effect of the dolphin swarm algorithm. The results show that in most cases, the dolphin swarm algorithm performs better. The dolphin swarm algorithm possesses some great features, such as first-slow-then-fast convergence, periodic convergence, local-optimum-free, and no specific demand on benchmark functions. Moreover, the dolphin swarm algorithm is particularly appropriate to optimization problems, with more calls of fitness functions and fewer individuals.