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Quantum-inspired evolutionary algorithms, one of the three main research areas related to the complex interaction between quantum computing and evolutionary algorithms, are receiving renewed attention. A quantum-inspired evolutionary algorithm is a new evolutionary algorithm for a classical computer rather than for quantum mechanical hardware.
Abstract: an improved quantum-inspired evolutionary algorithm (iqea) is presented in this paper to improve the clustering result of a data clustering problem. Like the other qea-based algorithms, the iqea uses q-bits to denote the state of a quantum particle and q-gate as an evolutionary operator to guide the search directions.
The quantum-inspired evolutionary algorithm (qea) applies quantum computing principles to enhance classical evolutionary algorithms (eas).
This article presents a new approach for solving unit commitment problems using a quantum-inspired evolutionary algorithm.
Improved quantum-inspired evolutionary algorithm with diversity information applied to economic dispatch problem with prohibited operating zones.
In [10-13], quantum computing was used to improve the original intelligent swarm optimization algorithm.
Quantum inspired evolutionary algorithms with improved rotation gates for real-coded synthetic and real world optimization problems joe wright, ivan jordanov* school of computing, university of portsmouth, lion terrace, portsmouth, po1 3he, uk; emails: jonathan.
Based on quantum-inspired evolutionary algorithm (qea), meqea introduces multi-granularity evolution mechanism which allows different chromosomes,.
In this paper we propose an improved quantum-inspired evo- lutionary algorithm (qea) for various combinatorial optimiza- tion problems applied in power.
A quantum-inspired evolutionary algorithm is a new evolutionary algorithm for a classical computer rather than for quantum mechanical hardware. This paper provides a unified framework and a comprehensive survey of recent work in this rapidly growing field.
This paper proposes a novel evolutionary algorithm inspired by quantum computing, as a variation operator to drive the individuals toward better solutions.
Parallel improved quantum inspired evolutionary algorithm to solve large size better than feasibility rules for constraint handling in evolutionary algorithms?.
To improve the performance of quantum-inspired evolutionary algorithm based on p systems (qeps), this paper presents an improved qeps with a dynamic membrane structure (qeps-dms) to solve knapsack problems. Qeps-dms combines quantum-inspired evolutionary algorithms (qieas) with a p system with a dynamic membrane structure.
Sented based on quantum-inspired evolutionary algorithm (qea). Qea with quantum chromosome and quantum mutation has better global search capacity.
This process may lead to the evolution of populations of individuals that are better suited to their environment than the in- dividuals from which they were created,.
[32] obtained very promising results using a new variant of the memetic algorithm for qap, where a solution created by the crossover operator is improved using.
Qea and qeaps imitationally use quantum bits as genes and superposition states in quantum computation.
Quantum-inspired multi-objective evolutionary algorithms for decision making: to avoid this disadvantage, some improvements have been introduced.
In this paper, a new improved quantum evolution algorithm (iqea) with a mixed local ters together, so quantum-inspired algorithm is also called the quantum.
An improved quantum-inspired evolutionary algorithm is presented in this paper. Quantum angle is adopted to present the quantum bit in the proposed algorithm. A novel quantum rotation gate strategy is adopted to adjust the direction of the quantum gate which is used to update the quantum population. The step size is adaptively adjusted rather than a fixed angle.
The quantum inspired evolutionary algorithms (qiea) were originally used for solving binary encoded problems and their signature features follow superposition of multiple states on a quantum bit and a rotation gate. In order to apply this paradigm to real value problems, we propose two quantum methods half significant bit (hsb) and stepwise.
Continued and rapid improvement in evolutionary algorithms has made them suitable technologies for tackling many difficult optimization problems.
An improved quantum-inspired evolutionary algorithm is proposed for solving mixed discrete-continuous nonlinear problems in engineering design. The proposed latin square quantum-inspired evolutionary algorithm (lsqea) combines latin squares and quantum-inspired genetic algorithm (qga).
Jan 22, 2021 combining the improved quantum‐inspired evolutionary algorithm with receding horizon optimization, we propose the separation method based.
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