Enhanced Quantum Evolutionary Algorithms for Difficult Knapsack Problems
نویسندگان
چکیده
Difficult knapsack problems are problems that are expressly designed to be difficult. In this paper, enhanced Quantum Evolutionary Algorithms are designed and their application is presented for the solution of the DKPs. The algorithms are general enough and can be used with advantage in other subset selection problems.
منابع مشابه
BQIABC: A new Quantum-Inspired Artificial Bee Colony Algorithm for Binary Optimization Problems
Artificial bee colony (ABC) algorithm is a swarm intelligence optimization algorithm inspired by the intelligent behavior of honey bees when searching for food sources. The various versions of the ABC algorithm have been widely used to solve continuous and discrete optimization problems in different fields. In this paper a new binary version of the ABC algorithm inspired by quantum computing, c...
متن کاملBalanced Quantum-Inspired Evolutionary Algorithm for Multiple Knapsack Problem
0/1 Multiple Knapsack Problem, a generalization of more popular 0/1 Knapsack Problem, is NP-hard and considered harder than simple Knapsack Problem. 0/1 Multiple Knapsack Problem has many applications in disciplines related to computer science and operations research. Quantum Inspired Evolutionary Algorithms (QIEAs), a subclass of Evolutionary algorithms, are considered effective to solve diffi...
متن کاملSolution of "Hard" Knapsack Instances Using Quantum Inspired Evolutionary Algorithm
Knapsack Problem (KP) is a popular combinatorial optimization problem having application in many technical and economic areas. Several attempts have been made in past to solve the problem. Various exact and non-exact approaches exist to solve KP. Exact algorithms for KP are based on either branch and bound or dynamic programming technique. Heuristics exist which solve KP non-exactly in lesser t...
متن کاملA New Quantum Immune Evolutionary Algorithm and Its Application
From recent research on combinatorial optimization of the complex function, quantum-inspired evolutionary algorithms (QEAs) were proved to be better than traditional evolutionary algorithms. However, they are easy to be trapped to prematurity, and the operations in QEAs lack the capability of meeting an actual situation, so that some torpidity often appears when solving problems. In this paper,...
متن کاملA Case for Codons in Evolutionary Algorithms
A new method is developed for representation and encoding in population-based evolutionary algorithms. The method is inspired by the biological genetic code and utilizes a many-to-one, codon-based, genotype-tophenotype translation scheme. A genetic algorithm was implemented with this codon-based representation using three different codon translation tables, each with different characteristics. ...
متن کامل