Pareto Neural Model for Finding Task Allocation
نویسندگان
چکیده
In this paper, the Hopfield model of artificial neural networks called HANNs for finding some task allocations in multiple computer systems have been proposed. A multiobjective optimisation problem with two criteria has been considered. Resource constraints have been assumed, too. Both the cost of parallel program execution and the cost of computers have been minimised. Two models of neural networks for minimisation of the computer cost and for minimisation of the cost of parallel program execution have been designed. Moreover, HANN for finding local Paretooptimal solutions has been considered. Finally, some simulation results related to minimisation of the energy function for constructed neural networks have been included. Especially, a trajectory of energy function obtained during finding Pareto-optimal task allocation has been presented.
منابع مشابه
Developing a bi-objective optimization model for solving the availability allocation problem in repairable series–parallel systems by NSGA II
Bi-objective optimization of the availability allocation problem in a series–parallel system with repairable components is aimed in this paper. The two objectives of the problem are the availability of the system and the total cost of the system. Regarding the previous studies in series–parallel systems, the main contribution of this study is to expand the redundancy allocation problems to syst...
متن کاملThree New Complexity Results for Resource Allocation Problems
We prove the following results for task allocation of indivisible resources: • The problem of finding a leximin-maximal resource allocation is in P if the agents have max-utility functions and atomic demands. • Deciding whether a resource allocation is Pareto-optimal is coNP-complete for agents with (1-)additive utility functions. • Deciding whether there exists a Pareto-optimal and envy-free r...
متن کاملCombining Fairness and Efficiency in Dynamic Task Allocation with Spatial and Temporal Constraints
Realistic multiagent team applications often feature distributed dynamic environments with soft deadlines that penalize late execution of tasks. This puts a premium on quickly allocating tasks to agents, but finding the optimal allocation is NP-hard due to temporal and spatial constraints that require tasks to be executed sequentially by agents. We propose a novel task allocation algorithm that...
متن کاملStatic Task Allocation in Distributed Systems Using Parallel Genetic Algorithm
Over the past two decades, PC speeds have increased from a few instructions per second to several million instructions per second. The tremendous speed of today's networks as well as the increasing need for high-performance systems has made researchers interested in parallel and distributed computing. The rapid growth of distributed systems has led to a variety of problems. Task allocation is a...
متن کاملA DSS-Based Dynamic Programming for Finding Optimal Markets Using Neural Networks and Pricing
One of the substantial challenges in marketing efforts is determining optimal markets, specifically in market segmentation. The problem is more controversial in electronic commerce and electronic marketing. Consumer behaviour is influenced by different factors and thus varies in different time periods. These dynamic impacts lead to the uncertain behaviour of consumers and therefore harden the t...
متن کامل