A quantum multi-objective optimization algorithm based on harmony search method

Hesar, Alireza Sadeghi; Tabbakh, Seyed Reza Kamel; Houshmand, Mahboobeh

doi:10.1007/s00500-021-05799-x

Cited by 17 publications

(5 citation statements)

References 52 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…If the qubit is measured in the computational, i.e., {|0 , |1 } basis, the classic outcome of 0 is observed with the probability of |α| 2 and the classic outcome of 1 is observed with the probability of |β| 2 . If 0 is observed, the state of the qubit after the measurement collapses to |0 , otherwise, it collapses to |1 [50].…”

Section: Quantum Computing Basicsmentioning

confidence: 99%

“…In [27], a quantum-inspired metaheuristic is used for quantum circuit synthesis. Other applications are feature selection [28,29], fuzzy c-means clustering [30], stock market portfolio optimization [31], flight control optimization [32,33], antenna positioning problems [34], airport gate allocation [35], and multi-objective optimization [36]. A comprehensive review of quantum-inspired metaheuristics and their variants is presented in [37,38].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Modified Quantum-Inspired Genetic Algorithm Using Lengthening Chromosome Size and an Adaptive Look-Up Table to Avoid Local Optima

Hakemi,

Houshmand,

Hosseini

et al. 2023

Axioms

Self Cite

View full text Add to dashboard Cite

The quantum-inspired genetic algorithm (QGA), which combines quantum mechanics concepts and GA to enhance search capability, has been popular and provides an efficient search mechanism. This paper proposes a modified QGA, called dynamic QGA (DQGA). The proposed algorithm utilizes a lengthening chromosome strategy for a balanced and smooth transition between exploration and exploitation phases to avoid local optima and premature convergence. Apart from that, a novel adaptive look-up table for rotation gates is presented to boost the algorithm’s optimization abilities. To evaluate the effectiveness of these ideas, DQGA is tested by various mathematical benchmark functions as well as real-world constrained engineering problems against several well-known and state-of-the-art algorithms. The obtained results indicate the merits of the proposed algorithm and its superiority for solving multimodal benchmark functions and real-world constrained engineering problems.

show abstract

Section: Quantum Computing Basicsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Modified Quantum-Inspired Genetic Algorithm Using Lengthening Chromosome Size and an Adaptive Look-Up Table to Avoid Local Optima

Hakemi,

Houshmand,

Hosseini

et al. 2023

Axioms

Self Cite

View full text Add to dashboard Cite

show abstract

“…This article proposed a corrosion risk assessment model for automotive materials based on intelligent algorithms. This model is trained using machine learning algorithms and can automatically identify and evaluate the corrosion risk of automotive materials [13][14]. Firstly, a large amount of corrosion data was collected, including information on corrosion types, degrees, and locations.…”

Section: Risk Assessment Model Designmentioning

confidence: 99%

Intelligent Algorithms for Air Pollution Corrosion Risk Assessment and Management of Automotive Materials

2023

AJMC

View full text Add to dashboard Cite

The evaluation of corrosion risk of automotive materials refers to the assessment of the potential risk of corrosion of automotive materials during use, in order to determine their impact on automotive performance and safety. At present, the methods for assessing and managing the corrosion risk of automotive materials are mainly based on expert experience and manual measurement. These methods have problems such as inaccurate evaluation results and low efficiency. With the development of artificial intelligence technology, intelligent algorithms are gradually being applied in the automotive field. Therefore, this article proposed a risk assessment model for automotive material corrosion based on intelligent algorithms, aiming to predict risks in advance and reduce unnecessary losses. This article mainly applied experimental analysis and algorithm comparison to analyze the influencing factors of automotive material corrosion, and compared the performance of different intelligent algorithms. The experimental results showed that the minimum error rate of the support vector machine algorithm was 9.8%, and further improvement of the intelligent algorithm was needed.

show abstract

“…Fan et al [39] established a bi-level optimization model based on the quantum evolutionary algorithm and multi-objective programming to solve the problem of regional integrated energy systems. Hesar et al [40] proposed a quantum-inspired multi-objective harmony search algorithm to solve multi-objective optimization problems. In this algorithm, a new quantum mutation strategy is proposed, which is a combination of harmony improvisation operators and a quantum adaptive rotation gate.…”

Section: Quantum Computingmentioning

confidence: 99%

Multi-Objective Quantum-Inspired Seagull Optimization Algorithm

Wang

Ahmad

et al. 2022

Electronics

View full text Add to dashboard Cite

Objective solutions of multi-objective optimization problems (MOPs) are required to balance convergence and distribution to the Pareto front. This paper proposes a multi-objective quantum-inspired seagull optimization algorithm (MOQSOA) to optimize the convergence and distribution of solutions in multi-objective optimization problems. The proposed algorithm adopts opposite-based learning, the migration and attacking behavior of seagulls, grid ranking, and the superposition principles of quantum computing. To obtain a better initialized population in the absence of a priori knowledge, an opposite-based learning mechanism is used for initialization. The proposed algorithm uses nonlinear migration and attacking operation, simulating the behavior of seagulls for exploration and exploitation. Moreover, the real-coded quantum representation of the current optimal solution and quantum rotation gate are adopted to update the seagull population. In addition, a grid mechanism including global grid ranking and grid density ranking provides a criterion for leader selection and archive control. The experimental results of the IGD and Spacing metrics performed on ZDT, DTLZ, and UF test suites demonstrate the superiority of MOQSOA over NSGA-II, MOEA/D, MOPSO, IMMOEA, RVEA, and LMEA for enhancing the distribution and convergence performance of MOPs.

show abstract

A quantum multi-objective optimization algorithm based on harmony search method

Cited by 17 publications

References 52 publications

A Modified Quantum-Inspired Genetic Algorithm Using Lengthening Chromosome Size and an Adaptive Look-Up Table to Avoid Local Optima

A Modified Quantum-Inspired Genetic Algorithm Using Lengthening Chromosome Size and an Adaptive Look-Up Table to Avoid Local Optima

Intelligent Algorithms for Air Pollution Corrosion Risk Assessment and Management of Automotive Materials

Multi-Objective Quantum-Inspired Seagull Optimization Algorithm

Contact Info

Product

Resources

About