Minimum Span Frequency Assignment Based on a Multiagent Evolutionary Algorithm

2011 ◽  
Vol 2 (3) ◽  
pp. 29-42
Author(s):  
Jing Liu ◽  
Jinshu Li ◽  
Weicai Zhong ◽  
Li Zhang ◽  
Ruochen Liu
Keyword(s):  
Evolutionary Algorithms ◽  
Evolutionary Algorithm ◽  
Frequency Spectrum ◽  
Multiagent Systems ◽  
Experimental Results ◽  
Frequency Assignment ◽  
Assignment Problems ◽  
The Cost ◽  
Very High

In frequency assignment problems (FAPs), separation of the frequencies assigned to the transmitters is necessary to avoid the interference. However, unnecessary separation causes an excess requirement of spectrum, the cost of which may be very high. Since FAPs are closely related to T-coloring problems (TCP), multiagent systems and evolutionary algorithms are combined to form a new algorithm for minimum span FAPs on the basis of the model of TCP, which is named as Multiagent Evolutionary Algorithm for Minimum Span FAPs (MAEA-MSFAPs). The objective of MAEA-MSFAPs is to minimize the frequency spectrum required for a given level of reception quality over the network. In MAEA-MSFAPs, all agents live in a latticelike environment. Making use of the designed behaviors, MAEA-MSFAPs realizes the ability of agents to sense and act on the environment in which they live. During the process of interacting with the environment and other agents, each agent increases the energy as much as possible so that MAEA-MSFAPs can find the optima. Experimental results on TCP with different sizes and Philadelphia benchmark for FAPs show that MAEA-MSFAPs have a good performance and outperform the compared methods.

Minimum Span Frequency Assignment Based on a Multiagent Evolutionary Algorithm

2013 ◽  
pp. 223-236
Author(s):  
Jing Liu ◽  
Jinshu Li ◽  
Weicai Zhong ◽  
Li Zhang ◽  
Ruochen Liu
Keyword(s):  
Evolutionary Algorithms ◽  
Evolutionary Algorithm ◽  
Frequency Spectrum ◽  
Multiagent Systems ◽  
Experimental Results ◽  
Frequency Assignment ◽  
Assignment Problems ◽  
The Cost ◽  
Very High

In frequency assignment problems (FAPs), separation of the frequencies assigned to the transmitters is necessary to avoid the interference. However, unnecessary separation causes an excess requirement of spectrum, the cost of which may be very high. Since FAPs are closely related to T-coloring problems (TCP), multiagent systems and evolutionary algorithms are combined to form a new algorithm for minimum span FAPs on the basis of the model of TCP, which is named as Multiagent Evolutionary Algorithm for Minimum Span FAPs (MAEA-MSFAPs). The objective of MAEA-MSFAPs is to minimize the frequency spectrum required for a given level of reception quality over the network. In MAEA-MSFAPs, all agents live in a latticelike environment. Making use of the designed behaviors, MAEA-MSFAPs realizes the ability of agents to sense and act on the environment in which they live. During the process of interacting with the environment and other agents, each agent increases the energy as much as possible so that MAEA-MSFAPs can find the optima. Experimental results on TCP with different sizes and Philadelphia benchmark for FAPs show that MAEA-MSFAPs have a good performance and outperform the compared methods.

scholarly journals Evolutionary Manytasking Optimization Based on Symbiosis in Biocoenosis

2019 ◽  
Vol 33 ◽  
pp. 4295-4303 ◽  
Author(s):  
Rung-Tzuo Liaw ◽  
Chuan-Kang Ting
Keyword(s):  
Evolutionary Algorithms ◽  
Evolutionary Algorithm ◽  
State Of The Art ◽  
The State ◽  
Experimental Results ◽  
Benchmark Problems ◽  
Good Estimator ◽  
Multiple Tasks ◽  
Effectiveness And Efficiency ◽  
Evolutionary Multitasking

Evolutionary multitasking is a significant emerging search paradigm that utilizes evolutionary algorithms to concurrently optimize multiple tasks. The multi-factorial evolutionary algorithm renders an effectual realization of evolutionary multitasking on two or three tasks. However, there remains room for improvement on the performance and capability of evolutionary multitasking. Beyond three tasks, this paper proposes a novel framework, called the symbiosis in biocoenosis optimization (SBO), to address evolutionary many-tasking optimization. The SBO leverages the notion of symbiosis in biocoenosis for transferring information and knowledge among different tasks through three major components: 1) transferring information through inter-task individual replacement, 2) measuring symbiosis through intertask paired evaluations, and 3) coordinating the frequency and quantity of transfer based on symbiosis in biocoenosis. The inter-task individual replacement with paired evaluations caters for estimation of symbiosis, while the symbiosis in biocoenosis provides a good estimator of transfer. This study examines the effectiveness and efficiency of the SBO on a suite of many-tasking benchmark problems, designed to deal with 30 tasks simultaneously. The experimental results show that SBO leads to better solutions and faster convergence than the state-of-the-art evolutionary multitasking algorithms. Moreover, the results indicate that SBO is highly capable of identifying the similarity between problems and transferring information appropriately.

scholarly journals On the Choice of the Offspring Population Size in Evolutionary Algorithms

2005 ◽  
Vol 13 (4) ◽  
pp. 413-440 ◽  
Author(s):  
Thomas Jansen ◽  
Kenneth A. De Jong ◽  
Ingo Wegener
Keyword(s):  
Evolutionary Algorithms ◽  
Evolutionary Algorithm ◽  
Population Size ◽  
Diffi Cult ◽  
Very High

Evolutionary algorithms (EAs) generally come with a large number of parameters that have to be set before the algorithm can be used. Finding appropriate settings is a diffi- cult task. The influence of these parameters on the efficiency of the search performed by an evolutionary algorithm can be very high. But there is still a lack of theoretically justified guidelines to help the practitioner find good values for these parameters. One such parameter is the offspring population size. Using a simplified but still realistic evolutionary algorithm, a thorough analysis of the effects of the offspring population size is presented. The result is a much better understanding of the role of offspring population size in an EA and suggests a simple way to dynamically adapt this parameter when necessary.

Composite Evolutionary Algorithm for Constrained Optimization

2012 ◽  
Vol 220-223 ◽  
pp. 2846-2851
Author(s):  
Si Lian Xie ◽  
Tie Bin Wu ◽  
Shui Ping Wu ◽  
Yun Lian Liu
Keyword(s):  
Evolutionary Algorithms ◽  
Constrained Optimization ◽  
Evolutionary Algorithm ◽  
Selection Criteria ◽  
Optimization Problems ◽  
Experimental Results ◽  
Constrained Optimization Problems ◽  
Mutation Operators ◽  
Single Operator ◽  
Algorithm Framework

Evolutionary algorithms are amongst the best known methods of solving difficult constrained optimization problems, for which traditional methods are not applicable. Due to the variability of characteristics in different constrained optimization problems, no single evolutionary with single operator performs consistently over a range of problems. We introduce an algorithm framework that uses multiple search operators in each generation. A composite evolutionary algorithm is proposed in this paper and combined feasibility rule to solve constrained optimization problems. The proposed evolutionary algorithm combines three crossover operators with two mutation operators. The selection criteria based on feasibility of individual is used to deal with the constraints. The proposed method is tested on five well-known benchmark constrained optimization problems, and the experimental results show that it is effective and robust

scholarly journals Cost per DALY averted in low, middle- and high-income countries: evidence from the global burden of disease study to estimate the cost-effectiveness thresholds

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Rajabali Daroudi ◽  
Ali Akbari Sari ◽  
Azin Nahvijou ◽  
Ahmad Faramarzi
Keyword(s):  
Cost Effectiveness ◽  
Inverse Correlation ◽  
Gdp Per Capita ◽  
Middle Income ◽  
Capita Health Expenditure ◽  
Per Capita ◽  
Healthcare Interventions ◽  
Disease Study ◽  
The Cost ◽  
Very High

Abstract Background Determining the cost-effectiveness thresholds for healthcare interventions has been a severe challenge for policymakers, especially in low- and middle-income countries. This study aimed to estimate the cost per disability-adjusted life-year (DALY) averted for countries with different levels of Human Development Index (HDI) and Gross Domestic Product (GDP). Methods The data about DALYs, per capita health expenditure (HE), HDI, and GDP per capita were extracted for 176 countries during the years 2000 to 2016. Then we examined the trends on these variables. Panel regression analysis was performed to explore the correlation between DALY and HE per capita. The results of the regression models were used to calculate the cost per DALY averted for each country. Results Age-standardized rate (ASR) DALY (DALY per 100,000 population) had a nonlinear inverse correlation with HE per capita and a linear inverse correlation with HDI. One percent increase in HE per capita was associated with an average of 0.28, 0.24, 0.18, and 0.27% decrease on the ASR DALY in low HDI, medium HDI, high HDI, and very high HDI countries, respectively. The estimated cost per DALY averted was $998, $6522, $23,782, and $69,499 in low HDI, medium HDI, high HDI, and very high HDI countries. On average, the cost per DALY averted was 0.34 times the GDP per capita in low HDI countries. While in medium HDI, high HDI, and very high HDI countries, it was 0.67, 1.22, and 1.46 times the GDP per capita, respectively. Conclusions This study suggests that the cost-effectiveness thresholds might be less than a GDP per capita in low and medium HDI countries and between one and two GDP per capita in high and very high HDI countries.

scholarly journals Drift Analysis and Evolutionary Algorithms Revisited

2018 ◽  
Vol 27 (4) ◽  
pp. 643-666 ◽  
Author(s):  
J. LENGLER ◽  
A. STEGER
Keyword(s):  
Evolutionary Algorithms ◽  
Evolutionary Algorithm ◽  
Random Search ◽  
Optimization Algorithms ◽  
Simple Algorithm ◽  
Linear Functions ◽  
Search Point ◽  
Drift Analysis ◽  
Fixed Constant ◽  
Greedy Optimization

One of the easiest randomized greedy optimization algorithms is the following evolutionary algorithm which aims at maximizing a function f: {0,1}n → ℝ. The algorithm starts with a random search point ξ ∈ {0,1}n, and in each round it flips each bit of ξ with probability c/n independently at random, where c > 0 is a fixed constant. The thus created offspring ξ' replaces ξ if and only if f(ξ') ≥ f(ξ). The analysis of the runtime of this simple algorithm for monotone and for linear functions turned out to be highly non-trivial. In this paper we review known results and provide new and self-contained proofs of partly stronger results.

scholarly journals An automated system analysis and design tool for spacecrafts ceas space journal

2021 ◽  
Author(s):  
Manfred Ehresmann ◽  
Georg Herdrich ◽  
Stefanos Fasoulas
Keyword(s):  
Evolutionary Algorithms ◽  
Evolutionary Algorithm ◽  
Degrees Of Freedom ◽  
System Analysis ◽  
Propulsion System ◽  
Optimal System ◽  
Automated System ◽  
Processing Unit ◽  
Data Set ◽  
Analysis And Design

AbstractIn this paper, a generic full-system estimation software tool is introduced and applied to a data set of actual flight missions to derive a heuristic for system composition for mass and power ratios of considered sub-systems. The capability of evolutionary algorithms to analyse and effectively design spacecraft (sub-)systems is shown. After deriving top-level estimates for each spacecraft sub-system based on heuristic heritage data, a detailed component-based system analysis follows. Various degrees of freedom exist for a hardware-based sub-system design; these are to be resolved via an evolutionary algorithm to determine an optimal system configuration. A propulsion system implementation for a small satellite test case will serve as a reference example of the implemented algorithm application. The propulsion system includes thruster, power processing unit, tank, propellant and general power supply system masses and power consumptions. Relevant performance parameters such as desired thrust, effective exhaust velocity, utilised propellant, and the propulsion type are considered as degrees of freedom. An evolutionary algorithm is applied to the propulsion system scaling model to demonstrate that such evolutionary algorithms are capable of bypassing complex multidimensional design optimisation problems. An evolutionary algorithm is an algorithm that uses a heuristic to change input parameters and a defined selection criterion (e.g., mass fraction of the system) on an optimisation function to refine solutions successively. With sufficient generations and, thereby, iterations of design points, local optima are determined. Using mitigation methods and a sufficient number of seed points, a global optimal system configurations can be found.

scholarly journals The Application of Electromagnetic Sensors for Determination of Cherenkov Cone Inside and in the Vicinity of the Detector Volume in Any Environment Known

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 992
Author(s):  
Valeriu Savu ◽  
Mădălin Ion Rusu ◽  
Dan Savastru
Keyword(s):  
Energy Levels ◽  
Cherenkov Detector ◽  
Electromagnetic Sensors ◽  
The Cost ◽  
Information Errors ◽  
The Universe ◽  
Large Frequency ◽  
Very High ◽  
Detector Volume

The neutrinos of cosmic radiation, due to interaction with any known medium in which the Cherenkov detector is used, produce energy radiation phenomena in the form of a Cherenkov cone, in very large frequency spectrum. These neutrinos carry with them the information about the phenomena that produced them and by detecting the electromagnetic energies generated by the Cherenkov cone, we can find information about the phenomena that formed in the universe, at a much greater distance, than possibility of actually detection with current technologies. At present, a very high number of sensors for detection electromagnetic energy is required. Thus, some sensors may detect very low energy levels, which can lead to the erroneous determination of the Cherenkov cone, thus leading to information errors. As a novelty, we propose, to use these sensors for determination of the dielectrically permittivity of any known medium in which the Cherenkov detector is used, by preliminary measurements, the subsequent simulation of the data and the reconstruction of the Cherenkov cone, leading to a significant reduction of problems and minimizing the number of sensors, implicitly the cost reductions. At the same time, we offer the possibility of reconstructing the Cherenkov cone outside the detector volume.

scholarly journals Addition of Flucytosine to Fluconazole for the Treatment of Cryptococcal Meningitis in Africa: A Multicountry Cost-effectiveness Analysis

2019 ◽  
Vol 70 (1) ◽  
pp. 26-29 ◽  
Author(s):  
Tinevimbo Shiri ◽  
Angela Loyse ◽  
Lawrence Mwenge ◽  
Tao Chen ◽  
Shabir Lakhi ◽  
...  
Keyword(s):  
Sensitivity Analysis ◽  
Cost Effectiveness ◽  
Cryptococcal Meningitis ◽  
Cost Effective ◽  
Immunodeficiency Virus ◽  
The Mean ◽  
The Cost ◽  
The Impact ◽  
Costing Analysis ◽  
Very High

Abstract Background Mortality from cryptococcal meningitis remains very high in Africa. In the Advancing Cryptococcal Meningitis Treatment for Africa (ACTA) trial, 2 weeks of fluconazole (FLU) plus flucytosine (5FC) was as effective and less costly than 2 weeks of amphotericin-based regimens. However, many African settings treat with FLU monotherapy, and the cost-effectiveness of adding 5FC to FLU is uncertain. Methods The effectiveness and costs of FLU+5FC were taken from ACTA, which included a costing analysis at the Zambian site. The effectiveness of FLU was derived from cohorts of consecutively enrolled patients, managed in respects other than drug therapy, as were participants in ACTA. FLU costs were derived from costs of FLU+5FC in ACTA, by subtracting 5FC drug and monitoring costs. The cost-effectiveness of FLU+5FC vs FLU alone was measured as the incremental cost-effectiveness ratio (ICER). A probabilistic sensitivity analysis assessed uncertainties and a bivariate deterministic sensitivity analysis examined the impact of varying mortality and 5FC drug costs on the ICER. Results The mean costs per patient were US $847 (95% confidence interval [CI] $776–927) for FLU+5FC, and US $628 (95% CI $557–709) for FLU. The 10-week mortality rate was 35.1% (95% CI 28.9–41.7%) with FLU+5FC and 53.8% (95% CI 43.1–64.1%) with FLU. At the current 5FC price of US $1.30 per 500 mg tablet, the ICER of 5FC+FLU versus FLU alone was US $65 (95% CI $28–208) per life-year saved. Reducing the 5FC cost to between US $0.80 and US $0.40 per 500 mg resulted in an ICER between US $44 and US $28 per life-year saved. Conclusions The addition of 5FC to FLU is cost-effective for cryptococcal meningitis treatment in Africa and, if made available widely, could substantially reduce mortality rates among human immunodeficiency virus–infected persons in Africa.

A Determinant Elimination Method for Bottleneck Assignment and Generalized Assignment Problems

2012 ◽  
Vol 239-240 ◽  
pp. 1522-1527
Author(s):  
Wen Bo Wu ◽  
Yu Fu Jia ◽  
Hong Xing Sun
Keyword(s):  
Computational Complexity ◽  
Optimization Algorithm ◽  
Assignment Problem ◽  
Numerical Experiments ◽  
High Efficiency ◽  
Synthesis Method ◽  
Assignment Problems ◽  
Generalized Assignment ◽  
The Cost ◽  
Time And Space Complexity

The bottleneck assignment (BA) and the generalized assignment (GA) problems and their exact solutions are explored in this paper. Firstly, a determinant elimination (DE) method is proposed based on the discussion of the time and space complexity of the enumeration method for both BA and GA problems. The optimization algorithm to the pre-assignment problem is then discussed and the adjusting and transformation to the cost matrix is adopted to reduce the computational complexity of the DE method. Finally, a synthesis method for both BA and GA problems is presented. The numerical experiments are carried out and the results indicate that the proposed method is feasible and of high efficiency.

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