The Cost-Sorted Distance Method for Identifying Minima Within Firefly Optimization Results: Application to Engineering Design

2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Christopher M. Elliott ◽  
Scott M. Ferguson ◽  
Gregory D. Buckner
Keyword(s):  
Optimization Method ◽  
Population Based ◽  
Benchmark Problems ◽  
Distance Method ◽  
Vector Population ◽  
Design Alternatives ◽  
Multiple Minima ◽  
Firefly Optimization ◽  
Euclidean Distances ◽  
The Cost

Abstract This paper provides a detailed description of the cost-sorted distance (CSD) method for visually and computationally identifying objective function minima within clustered population-based optimization results. CSD requires sorting the design vector population by cost and computing Euclidean distances between each pair of designs. It may be applied in conjunction with any population-based optimization method (e.g., particle swarm, genetic algorithm, simulated annealing, ant colony, firefly), but it is naturally compatible with the firefly algorithm (FA) because FA also requires the distances between each pair of design vectors and benefits from cost-sorting the population (the computational work required for CSD is a byproduct of FA). A modified FA is presented that uses CSD to more thoroughly search near potential minima and a systematic method for tuning the algorithm to reliably identify multiple minima is documented. The tuned algorithm's efficacy is demonstrated using a class of benchmark problems and a “real world” electromechanical design problem, where the identification of attractive design alternatives can be challenging.

An Improved Differential Evolution Algorithm for Dynamic Economic Dispatch of Power Systems

2011 ◽  
Vol 84-85 ◽  
pp. 706-710 ◽  
Author(s):  
Hong Feng Zheng
Keyword(s):  
Power Systems ◽  
Differential Evolution ◽  
Economic Dispatch ◽  
Differential Evolution Algorithm ◽  
Optimization Method ◽  
Population Based ◽  
Vector Population ◽  
Open Questions ◽  
Improved Differential Evolution Algorithm ◽  
Dynamic Economic Dispatch

Differential Evolution (DE), a vector population based stochastic optimization method has been introduced to the public in 1995. During the last 25 years research on and with DE has reached an impressive state, yet there are still many open questions, In this paper ,An improved differential evolution (IDE) algorithm was presented for power system Dynamic economic dispatch(IDED), Dynamic economic dispatch (DED), an extension of the economic dispatch problem, is a method of scheduling the online generators with a predicted load demand over acertain period of time taking into account the various constraints imposed on the system operation. The results indicate that IDE algorithm outperforms GA ,PSO and DE algorithms in solving DED problems.

scholarly journals Minimization over randomly selected lines

2013 ◽  
Vol 3 (2) ◽  
pp. 111-119 ◽  
Author(s):  
Ismet Sahin
Keyword(s):  
Cost Function ◽  
Optimization Method ◽  
Population Based ◽  
Quadratic Functions ◽  
Stopping Criterion ◽  
Crossover Operator ◽  
Selected Lines ◽  
Swarm Algorithms ◽  
Function Domain ◽  
The Cost

This paper presents a population-based evolutionary optimization method for minimizing a given cost function. The mutation operator of this method selects randomly oriented lines in the cost function domain, constructs quadratic functions interpolating the cost function at three different points over each line, and uses extrema of the quadratics as mutated points. The crossover operator modifies each mutated point based on components of two points in population, instead of one point as is usually performed in other evolutionary algorithms. The stopping criterion of this method depends on the number of almost degenerate quadratics. We demonstrate that the proposed method with these mutation and crossover operations achieves faster and more robust convergence than the well-known Differential Evolution and Particle Swarm algorithms.

scholarly journals An Adaptive Optimization Method Based on Learning Rate Schedule for Neural Networks

2021 ◽  
Vol 11 (2) ◽  
pp. 850
Author(s):  
Dokkyun Yi ◽  
Sangmin Ji ◽  
Jieun Park
Keyword(s):  
Artificial Intelligence ◽  
Cost Function ◽  
Numerical Experiments ◽  
Global Minimum ◽  
Optimization Method ◽  
Learning Method ◽  
Adaptive Optimization ◽  
The Cost ◽  
Proof Of Convergence ◽  
Learning Data

Artificial intelligence (AI) is achieved by optimizing the cost function constructed from learning data. Changing the parameters in the cost function is an AI learning process (or AI learning for convenience). If AI learning is well performed, then the value of the cost function is the global minimum. In order to obtain the well-learned AI learning, the parameter should be no change in the value of the cost function at the global minimum. One useful optimization method is the momentum method; however, the momentum method has difficulty stopping the parameter when the value of the cost function satisfies the global minimum (non-stop problem). The proposed method is based on the momentum method. In order to solve the non-stop problem of the momentum method, we use the value of the cost function to our method. Therefore, as the learning method processes, the mechanism in our method reduces the amount of change in the parameter by the effect of the value of the cost function. We verified the method through proof of convergence and numerical experiments with existing methods to ensure that the learning works well.

scholarly journals The association between varying levels of palliative care involvement on costs during terminal hospitalizations in Canada from 2012 to 2015

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Sarina R. Isenberg ◽  
Christopher Meaney ◽  
Peter May ◽  
Peter Tanuseputro ◽  
Kieran Quinn ◽  
...  
Keyword(s):  
Palliative Care ◽  
Metastatic Cancer ◽  
Estimating Equation ◽  
Population Based ◽  
Future Research ◽  
Chronic Obstructive ◽  
Discharge Abstract Database ◽  
High Involvement ◽  
Hospitalization Costs ◽  
The Cost

Abstract Background Inpatient palliative care is associated with lower inpatient costs; however, this has yet to be studied using a more nuanced, multi-tiered measure of inpatient palliative care and a national population-representative dataset. Using a population-based cohort of Canadians who died in hospital, our objectives were to: describe patients’ receipt of palliative care and active interventions in their terminal hospitalization; and examine the relationship between inpatient palliative care and hospitalization costs. Methods Retrospective cohort study using data from the Discharge Abstract Database in Canada between fiscal years 2012 and 2015. The cohort were Canadian adults (age ≥ 18 years) who died in hospital between April 1st, 2012 and March 31st, 2015 (N = 250,640). The exposure was level of palliative care involvement defined as: medium-high, low, or no palliative care. The main measure was acute care costs calculated using resource intensity weights multiplied by the cost of standard hospital stay, represented in 2014 Canadian dollars (CAD). Descriptive statistics were represented as median (IQR), and n(%). We modelled cost as a function of palliative care using a gamma generalized estimating equation (GEE) model, accounting for clustering by hospital. Results There were 250,640 adults who died in hospital. Mean age was 76 (SD 14), 47% were female. The most common comorbidities were: metastatic cancer (21%), heart failure (21%), and chronic obstructive pulmonary disease (16%). Of the decedents, 95,450 (38%) had no palliative care involvement, 98,849 (38%) received low involvement, and 60,341 (24%) received medium to high involvement. Controlling for age, sex, province and predicted hospital mortality risk at admission, the cost per day of a terminal hospitalization was: $1359 (95% CI 1323: 1397) (no involvement), $1175 (95% CI 1146: 1206) (low involvement), and $744 (95% CI 728: 760) (medium-high involvement). Conclusions Increased involvement of palliative care was associated with lower costs. Future research should explore whether this relationship holds for non-terminal hospitalizations, and whether palliative care in other settings impacts inpatient costs.

The estimated healthcare cost of diabetic retinopathy in Indonesia and its projection for 2025

2019 ◽  
Vol 104 (4) ◽  
pp. 487-492 ◽  
Author(s):  
Muhammad Bayu Sasongko ◽  
Firman Setya Wardhana ◽  
Gandhi Anandika Febryanto ◽  
Angela Nurini Agni ◽  
Supanji Supanji ◽  
...  
Keyword(s):  
Diabetic Retinopathy ◽  
Laser Treatment ◽  
Healthcare Cost ◽  
Cost Of Illness ◽  
Public Health Intervention ◽  
Transportation Cost ◽  
Population Based ◽  
State Budget ◽  
Illness Model ◽  
The Cost

PurposeTo estimate the total healthcare cost associated with diabetic retinopathy (DR) in type 2 diabetes in Indonesia and its projection for 2025.MethodsA prevalence-based cost-of-illness model was constructed from previous population-based DR study. Projection for 2025 was derived from estimated diabetes population in 2025. Direct treatment costs of DR were estimated from the perspective of healthcare. Patient perspective costs were obtained from thorough interview including only transportation cost and lost of working days related to treatment. We developed four cost-of-illness models according to DR severity level, DR without necessary treatment, needing laser treatment, laser +intravitreal (IVT) injection and laser + IVT +vitrectomy. All costs were estimated in 2017 US$.ResultsThe healthcare costs of DR in Indonesia were estimated to be $2.4 billion in 2017 and $8.9 billion in 2025. The total cost in 2017 consisted of the cost for no DR and mild–moderate non-proliferative DR (NPDR) requiring eye screening ($25.9 million), severe NPDR or proliferative DR (PDR) requiring laser treatment ($0.25 billion), severe NPDR or PDR requiring both laser and IVT injection ($1.75 billion) and advance level of PDR requiring vitrectomy ($0.44 billion).ConclusionsThe estimated healthcare cost of DR in Indonesia in 2017 was considerably high, nearly 2% of the 2017 national state budget, and projected to increase significantly to more than threefold in 2025. The highest cost may incur for DR requiring both laser and IVT injection. Therefore, public health intervention to delay or prevent severe DR may substantially reduce the healthcare cost of DR in Indonesia.

Evaluating Design Alternatives of an Extruded Food Production Line Using Simulation

SIMULATION ◽  
2002 ◽  
Vol 78 (10) ◽  
pp. 626-632 ◽  
Author(s):  
Stephen F. Owens ◽  
Reuven R. Levary
Keyword(s):  
Production Line ◽  
Food Production ◽  
System Throughput ◽  
Unit Operations ◽  
Manufacturing Line ◽  
Design Alternatives ◽  
Current Design ◽  
Current Production ◽  
The Cost ◽  
Line Design

Several designs of a manufacturing line for the production of extruded foods were developed and compared with the current production line design. The manufacturing line consisted of six unit operations: batching, grinding, extruding, drying, further processing, and packing. The authors performed the comparisons by simulating the current design and the proposed alternatives. The alternatives consisted of modifications to the extruder-dryer and/or packing line designs. The simulation results indicated that all the alternate designs roughly doubled system throughput compared to current designs but did not differ significantly in performance among themselves. The doubled output is worth about $2.5 million per year. This is easily enough to justify the cost of new equipment. One design would cost less to implement than the others and thus was the preferred option.

Optimization of Multistage Machining Systems, Part 1: Mathematical Solution

1992 ◽  
Vol 114 (4) ◽  
pp. 524-531 ◽  
Author(s):  
J. S. Agapiou
Keyword(s):  
Optimization Problem ◽  
Cutting Parameters ◽  
Optimization Method ◽  
Idle Time ◽  
Optimization Techniques ◽  
Machining Parameters ◽  
Machining Time ◽  
Physical Constraints ◽  
Time Requirements ◽  
The Cost

The optimization problem for multistage machining systems has been investigated. Due to uneven time requirements at different stages in manufacturing, there could be idle times at various stations. It may be advantageous to reduce the values of machining parameters in order to reduce the cost at stations that require less machining time. However, optimization techniques available through the literature do not effectively utilize the idle time for the different stations generated during the balancing of the system. Proposed in this paper is an optimization method which utilizes the idle time to the full extent at all machining stations, with the intention of improving tool life and thus achieving cost reduction. The mathematical analysis considers the optimization of the production cost with an equality constraint of zero idle time for the stations with idle time. Physical constraints regarding the cutting parameters, force, power, surface finish, etc., as they arise in different operations, are also considered. The aforementioned problem has been theoretically analyzed and a computational algorithm developed. The advantages and effectiveness of the proposed approach are finally established through an example.

scholarly journals Cloud Particles Differential Evolution Algorithm: A Novel Optimization Method for Global Numerical Optimization

2015 ◽  
Vol 2015 ◽  
pp. 1-36 ◽  
Author(s):  
Wei Li ◽  
Lei Wang ◽  
Quanzhu Yao ◽  
Qiaoyong Jiang ◽  
Lei Yu ◽  
...  
Keyword(s):  
Solid State ◽  
Differential Evolution ◽  
Liquid State ◽  
Differential Evolution Algorithm ◽  
Optimization Method ◽  
Benchmark Problems ◽  
Gaseous State ◽  
Mutation Strategy ◽  
Evolution Algorithm ◽  
Cloud Particles

We propose a new optimization algorithm inspired by the formation and change of the cloud in nature, referred to as Cloud Particles Differential Evolution (CPDE) algorithm. The cloud is assumed to have three states in the proposed algorithm. Gaseous state represents the global exploration. Liquid state represents the intermediate process from the global exploration to the local exploitation. Solid state represents the local exploitation. The best solution found so far acts as a nucleus. In gaseous state, the nucleus leads the population to explore by condensation operation. In liquid state, cloud particles carry out macrolocal exploitation by liquefaction operation. A new mutation strategy called cloud differential mutation is introduced in order to solve a problem that the misleading effect of a nucleus may cause the premature convergence. In solid state, cloud particles carry out microlocal exploitation by solidification operation. The effectiveness of the algorithm is validated upon different benchmark problems. The results have been compared with eight well-known optimization algorithms. The statistical analysis on performance evaluation of the different algorithms on 10 benchmark functions and CEC2013 problems indicates that CPDE attains good performance.

Convergence of Simulated Annealing with Feedback Temperature Schedules

1997 ◽  
Vol 11 (3) ◽  
pp. 279-304 ◽  
Author(s):  
M. Kolonko ◽  
M. T. Tran
Keyword(s):  
Simulated Annealing ◽  
Cost Function ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Optimization Method ◽  
Search Process ◽  
Local Optimum ◽  
Fixed Sequence ◽  
Temperature Parameter ◽  
The Cost

It is well known that the standard simulated annealing optimization method converges in distribution to the minimum of the cost function if the probability a for accepting an increase in costs goes to 0. α is controlled by the “temperature” parameter, which in the standard setup is a fixed sequence of values converging slowly to 0. We study a more general model in which the temperature may depend on the state of the search process. This allows us to adapt the temperature to the landscape of the cost function. The temperature may temporarily rise such that the process can leave a local optimum more easily. We give weak conditions on the temperature schedules such that the process of solutions finally concentrates near the optimal solutions. We also briefly sketch computational results for the job shop scheduling problem.

scholarly journals Is Routine Chest X-ray After Ultrasound and Intracavitary Electrocardiogram Guided Peripherally Inserted Central Catheter Insertion Choosing Wisely? A Population-based Retrospective Study of 2857 Patients With Cancer

2021 ◽  
Author(s):  
Yan-Fen Shen ◽  
Jing Dong ◽  
Xin-Peng Wang ◽  
Xiao-Zheng Wang ◽  
Yuan-Yuan Zheng ◽  
...  
Keyword(s):  
Venous Access ◽  
Population Based ◽  
X Ray ◽  
Patients With Cancer ◽  
Potential Risk Factors ◽  
Catheter Tip ◽  
Chest X Ray ◽  
Clinical Records ◽  
The Cost ◽  
Routine Chest

Abstract Background: In China, routine chest X-ray (CXR) is generally required for peripherally inserted central venous catheters (PICC) to determine the position of the catheter tip. The aim of this study is to assess the value of a routine post-procedural CXR in the era of ultrasound and intracavitary electrocardiography(IC-ECG) -guided PICC insertion. Methods: A retrospective population-based cohort study was conducted to review the clinical records of all patients who had PICCs in the Venous Access Center of Beijing Cancer Hospital between January 1, 2019 and June 30, 2020. The incidence of catheter misplacement after insertion was measured. A logistic regression analysis was performed to examine potential risk factors associated with PICC-related complications and a cost analysis to assess the economic impact of the use of CXR.Results: There were 2,857 samples from 2,647 patients included. The overall incidence of intraoperative and postoperative catheter misplacement was 7.4% (n=210) and 0.67% (n=19), respectively. There was a high risk of postoperative catheter misplacement when the left-arm was chosen for placement (OR: 10.478; 95% CI: 3.467-31.670; p<0.001). The cost of performing CXR for screening of PICC-related complications was $23,808 per year, and that of using CXR to diagnose one case of catheter misplacement was $1253.Conclusion: This study confirms that misplacement of PICCs guided by ultrasound and IC-ECG is rare and that postoperative CXR is very costly. In our setting, routine postoperative CXR is unnecessary and not a wise option.

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