scholarly journals Planning for Multiple Preferences versus Planning with No Preference

2012 ◽  
Vol 2012 ◽  
pp. 1-9
Author(s):  
Daniel Bryce
Keyword(s):  
Multiple Solutions ◽  
Search Algorithm ◽  
Resource Consumption ◽  
Decision Makers ◽  
Relative Performance ◽  
Empirical Comparison ◽  
Multiple Alternative ◽  
Nondominated Solution ◽  
Single Objective

Many planning applications must address conflicting plan objectives, such as cost, duration, and resource consumption, and decision makers want to know the possible tradeoffs. Traditionally, such problems are solved by invoking a single-objective algorithm (such as A*) on multiple, alternative preferences of the objectives to identify nondominated plans. The less-popular alternative is to delay such reasoning and directly optimize multiple plan objectives with a search algorithm like multiobjective A* (MOA*). The relative performance of these two approaches hinges upon the number of -values computed for individual search nodes. A* may revisit a node several times and compute a different -value each time. MOA* visits each node once and may compute some number of -values (each estimating the value of a different nondominated solution constructed from the node). While A* does not share -values between searches for different solutions, MOA* can sometimes find multiple solutions while computing a single -value per node. The results of extensive empirical comparison show that (i) the performance of multiple invocations of a single-objective A* versus a single invocation of MOA* is often worse in time and quality and (ii) that techniques for balancing per node cost and exploration are promising.

Impacts of Multiple Solutions on the Lackadaisical Quantum Walk Search Algorithm

2020 ◽  
pp. 122-135
Author(s):  
Jonathan H. A. de Carvalho ◽  
Luciano S. de Souza ◽  
Fernando M. de Paula Neto ◽  
Tiago A. E. Ferreira
Keyword(s):  
Multiple Solutions ◽  
Search Algorithm ◽  
Quantum Walk

scholarly journals Dynamic Service Function Chaining Orchestration in a Multi-Domain: A Heuristic Approach Based on SRv6

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6563
Author(s):  
Yutong Wu ◽  
Jinhe Zhou
Keyword(s):  
Virtual Machines ◽  
Search Algorithm ◽  
Resource Consumption ◽  
Network Function Virtualization ◽  
Ant Colony Optimization Algorithm ◽  
Network Resource ◽  
Load Pressure ◽  
Service Function ◽  
Network Function ◽  
Series Of Experiments

With the emergence of virtualization technology, Network Function Virtualization (NFV) and Software Defined Networking (SDN) make the network function abstract from the hardware and allow it to be run on virtual machines. These technologies can help to provide more efficient services to users by Service Function Chaining (SFC). The sequence of multiple VNFs required by network operators to perform traffic steering is called SFC. Mapping and deploying SFC on the physical network can enable users to obtain customized services in time. At present, a key problem in deploying SFC is how to reduce network resource consumption and load pressure while ensuring the corresponding services for users. In this paper, we first introduce an NFV architecture for SFC deployment, and illustrate the SFC orchestration process which is based on SRv6 in multi-domain scenario. Then, we propose an effective SFC dynamic orchestration algorithm. First, we use Breadth-First Search algorithm to traverse network and find the shortest path for deploying VNFs. Next, we use the improved Ant Colony Optimization algorithm to generate the optimal deployment scheme. Finally, we conduct a series of experiments to verify the performance of our algorithm. Compared with other deployment algorithms, the results show that our solution effectively optimizes end-to-end delay, bandwidth resource consumption and load balancing.

scholarly journals Balancing ecosystem services with energy and food security – Assessing trade-offs from reservoir operation and irrigation investments in Kenya's Tana Basin

2014 ◽  
Vol 18 (8) ◽  
pp. 3259-3277 ◽  
Author(s):  
A. P. Hurford ◽  
J. J. Harou
Keyword(s):  
Ecosystem Services ◽  
Optimal Allocation ◽  
Search Algorithm ◽  
Management Strategies ◽  
Decision Makers ◽  
Pareto Optimal ◽  
Management Problem ◽  
Economic Sectors ◽  
Trade Off ◽  
Trade Offs

Abstract. Competition for water between key economic sectors and the environment means agreeing allocations is challenging. Managing releases from the three major dams in Kenya's Tana River basin with its 4.4 million inhabitants, 567 MW of installed hydropower capacity, 33 000 ha of irrigation and ecologically important wetlands and forests is a pertinent example. This research seeks firstly to identify and help decision-makers visualise reservoir management strategies which result in the best possible (Pareto-optimal) allocation of benefits between sectors. Secondly, it seeks to show how trade-offs between achievable benefits shift with the implementation of proposed new rice, cotton and biofuel irrigation projects. To approximate the Pareto-optimal trade-offs we link a water resources management simulation model to a multi-criteria search algorithm. The decisions or "levers" of the management problem are volume-dependent release rules for the three major dams and extent of investment in new irrigation schemes. These decisions are optimised for eight objectives covering the provision of water supply and irrigation, energy generation and maintenance of ecosystem services. Trade-off plots allow decision-makers to assess multi-reservoir rule-sets and irrigation investment options by visualising their impacts on different beneficiaries. Results quantify how economic gains from proposed irrigation schemes trade-off against the disturbance of ecosystems and local livelihoods that depend on them. Full implementation of the proposed schemes is shown to come at a high environmental and social cost. The clarity and comprehensiveness of "best-case" trade-off analysis is a useful vantage point from which to tackle the interdependence and complexity of "water-energy-food nexus" resource security issues.

Extracting Cognitive Maps for Intelligent Conceptual Product Design

2014 ◽  
Vol 1037 ◽  
pp. 518-521
Author(s):  
Ming Wei Wang ◽  
Jing Tao Zhou
Keyword(s):  
Network Structure ◽  
Intelligent Design ◽  
Structure Learning ◽  
Search Algorithm ◽  
Cognitive Maps ◽  
Decision Makers ◽  
Hill Climbing ◽  
Design Decision ◽  
Mental Maps ◽  
Design Elements

Cognitive maps represent decision makers’ mental maps and their strategies, which are always uncertain, ambiguous and hard to be formalized. In order to make intelligent design decision-making, a Bayesian approach for constructing cognitive maps is proposed in this paper. The cognitive map is modeled compatible with a Bayesian Network. Then cause-effect mapping rules between design elements embedded in cognitive maps can be made explicit by means of network structure learning. A score-based greedy search algorithm is implemented for network structure learning, in which penalized mutual information is defined as the scoring metric and hill-climbing search algorithm is used to find the highest-scoring network. The eliminating loop operator is introduced into the algorithm according to the restriction of the edge directionality.

Multiobjective Optimization of Bioethanol Production via Hydrolysis Using Hopfield-Enhanced Differential Evolution

2014 ◽  
pp. 340-359 ◽  
Author(s):  
T. Ganesan ◽  
I. Elamvazuthi ◽  
K. Z. K. Shaari ◽  
P. Vasant
Keyword(s):  
Differential Evolution ◽  
Multiple Solutions ◽  
Heuristic Algorithms ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Pareto Frontier ◽  
Weighted Sum ◽  
Bioethanol Production ◽  
Hypervolume Indicator

Many industrial problems in process optimization are Multi-Objective (MO), where each of the objectives represents different facets of the issue. Thus, having in hand multiple solutions prior to selecting the best solution is a seminal advantage. In this chapter, the weighted sum scalarization approach is used in conjunction with three meta-heuristic algorithms: Differential Evolution (DE), Hopfield-Enhanced Differential Evolution (HEDE), and Gravitational Search Algorithm (GSA). These methods are then employed to trace the approximate Pareto frontier to the bioethanol production problem. The Hypervolume Indicator (HVI) is applied to gauge the capabilities of each algorithm in approximating the Pareto frontier. Some comparative studies are then carried out with the algorithms developed in this chapter. Analysis on the performance as well as the quality of the solutions obtained by these algorithms is shown here.

A New Strategy Based on GSABAT to Solve Single Objective Optimization Problem

2019 ◽  
Vol 10 (3) ◽  
pp. 1-22 ◽  
Author(s):  
H.A. Sattar ◽  
Alaa Cheetar ◽  
Iraq Tareq
Keyword(s):  
Optimization Problem ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Bat Algorithm ◽  
Local Optimum ◽  
Search Mechanism ◽  
All Solutions ◽  
New Strategy ◽  
Local Research ◽  
Single Objective

This article proposes a new strategy based on a hybrid method that combines the gravitational search algorithm (GSA) with the bat algorithm (BAT) to solve a single-objective optimization problem. It first runs GSA, followed by BAT as the second step. The proposed approach relies on a parameter between 0 and 1 to address the problem of falling into local research because the lack of a local search mechanism increases intensity search, whereas diversity remains high and easily falls into the local optimum. The improvement is equivalent to the speed of the original BAT. Access speed is increased for the best solution. All solutions in the population are updated before the end of the operation of the proposed algorithm. The diversification feature of BAT has solved the problem of weakness in diversity observed in the algorithm by applying the parameters used in BAT. Moreover, balance is achieved through the intensification properties of the algorithms.

What the Bayesian framework has contributed to understanding cognition: Causal learning as a case study

2011 ◽  
Vol 34 (4) ◽  
pp. 203-204
Author(s):  
Keith J. Holyoak ◽  
Hongjing Lu
Keyword(s):  
Bayesian Models ◽  
Bayesian Framework ◽  
Causal Learning ◽  
Empirical Comparison ◽  
Theoretical Understanding ◽  
Target Article ◽  
Multiple Alternative ◽  
Systematic Formulation

AbstractThe field of causal learning and reasoning (largely overlooked in the target article) provides an illuminating case study of how the modern Bayesian framework has deepened theoretical understanding, resolved long-standing controversies, and guided development of new and more principled algorithmic models. This progress was guided in large part by the systematic formulation and empirical comparison of multiple alternative Bayesian models.

An Improve Cuckoo Search Algorithm for Traveling Salesman Problems

2014 ◽  
Vol 651-653 ◽  
pp. 2291-2295
Author(s):  
Suo Nan Lengzhi ◽  
Yue Guang Li
Keyword(s):  
Multiple Solutions ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Traveling Salesman ◽  
Experimental Results ◽  
Traveling Salesman Problems ◽  
Position Value ◽  
Benchmark Instances ◽  
Simulation Results

In this paper, according to the characteristics of TSP. An improve Cuckoo Search Algorithm was used to solve the TSP, adopting the code rule of randomized key representation based on the smallest position value. The experimental results show that the new algorithm is successful in locating multiple solutions and has better accuracy, simulation results of benchmark instances validate the efficiency and superiority of Cuckoo Search Algorithm.

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