Chaotic Multi-Objective Particle Swarm Optimization Algorithm Incorporating Clone Immunity

Sun, Ying; Gao, Yuelin; Shi, Xudong

doi:10.3390/math7020146

Cited by 16 publications

(8 citation statements)

References 25 publications

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“…In order to obtain the balance between convergence and diversity, in [24], the Clone Immunity Chaotic Multi-Objective Particle Swarm Optimization (CICMOPSO) is proposed, where the points in a non-dominated solution are mapped to a parallel-cell coordinate system. In order to maintain and change the external archive, logistic mapping and a neighboring immune operator are employed.…”

Section: Multi-objective Particle Swarm Optimizationmentioning

confidence: 99%

Clustering Analysis for the Pareto Optimal Front in Multi-Objective Optimization

2022

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Bio-inspired algorithms are a suitable alternative for solving multi-objective optimization problems. Among different proposals, a widely used approach is based on the Pareto front. In this document, a proposal is made for the analysis of the optimal front for multi-objective optimization problems using clustering techniques. With this approach, an alternative is sought for further use and improvement of multi-objective optimization algorithms considering solutions and clusters found. To carry out the clustering, the methods k-means and fuzzy c-means are employed, in such a way that there are two alternatives to generate the possible clusters. Regarding the results, it is observed that both clustering algorithms perform an adequate separation of the optimal Pareto continuous fronts; for discontinuous fronts, k-means and fuzzy c-means obtain results that complement each other (there is no superior algorithm). In terms of processing time, k-means presents less execution time than fuzzy c-means.

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Section: Multi-objective Particle Swarm Optimizationmentioning

confidence: 99%

Clustering Analysis for the Pareto Optimal Front in Multi-Objective Optimization

2022

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“…Chaotic mappings have been widely used in the optimization of intelligent algorithms due to their regularity, randomness, and traversability [44], but different chaotic mappings have a great influence on the chaotic optimization process [45]. The Tent mapping has good traversal uniformity and fast iterations and produces a uniform distribution of chaotic sequences between [0, 1].…”

Section: Chaotic Opposition Learning Strategymentioning

confidence: 99%

A Multi-Strategy Marine Predator Algorithm and Its Application in Joint Regularization Semi-Supervised ELM

Yang

Xia

et al. 2021

Mathematics

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A novel semi-supervised learning method is proposed to better utilize labeled and unlabeled samples to improve classification performance. However, there is exists the limitation that Laplace regularization in a semi-supervised extreme learning machine (SSELM) tends to lead to poor generalization ability and it ignores the role of labeled information. To solve the above problems, a Joint Regularized Semi-Supervised Extreme Learning Machine (JRSSELM) is proposed, which uses Hessian regularization instead of Laplace regularization and adds supervised information regularization. In order to solve the problem of slow convergence speed and the easy to fall into local optimum of marine predator algorithm (MPA), a multi-strategy marine predator algorithm (MSMPA) is proposed, which first uses a chaotic opposition learning strategy to generate high-quality initial population, then uses adaptive inertia weights and adaptive step control factor to improve the exploration, utilization, and convergence speed, and then uses neighborhood dimensional learning strategy to maintain population diversity. The parameters in JRSSELM are then optimized using MSMPA. The MSMPA-JRSSELM is applied to logging oil formation identification. The experimental results show that MSMPA shows obvious superiority and strong competitiveness in terms of convergence accuracy and convergence speed. Also, the classification performance of MSMPA-JRSSELM is better than other classification methods, and the practical application is remarkable.

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“…To enhance the speed of convergence, QCEGA uses a clone operator to make clonal expansion of the outstanding individuals in the iterative process [34]. The probability of an individual being selected for cloning is determined by its advantage value.…”

Section: Clone Operatormentioning

confidence: 99%

Quantum Clone Elite Genetic Algorithm‐Based Evaluation Mechanism for Maximizing Network Efficiency in Soil Moisture Wireless Sensor Networks

2021

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In agriculture, soil moisture wireless sensor networks (SMWSNs) are used to monitor the growth of crops for obtaining higher yields. The purpose of this paper is to improve the network efficiency of SMWSNs. Therefore, we propose a novel network efficiency evaluation mechanism which is suitable for soil moisture sensors and design a sensor target allocation model (STAM) for the actual agricultural situation. After that, a quantum clone elite genetic algorithm (QCEGA) is proposed; then, QCEGA is applied to optimize the STAM for obtaining optimal results. QCEGA uses the parallel mechanism of quantum computing to encode individuals, integrates the quantum revolving gate in quantum computing and the concept of cloning in biology to avoid the algorithm from falling into local optimum, and applies the elite strategy to speed up the convergence of the algorithm. Subsequently, the proposed algorithm is compared with simulated annealing (SA) and particle swarm optimization (PSO). Under the novel network efficiency evaluation mechanism, the simulation results demonstrate that the network efficiency based on QCEGA is higher than that of SA and PSO; what is more, QCEGA has better convergence performance. In comparison with traditional wireless sensor network efficiency evaluation approaches, our methods are more in line with the development of modern agriculture and can effectively improve the efficiency of SMWSNs, thus ensuring that crops can have a better growth condition.

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Chaotic Multi-Objective Particle Swarm Optimization Algorithm Incorporating Clone Immunity

Cited by 16 publications

References 25 publications

Clustering Analysis for the Pareto Optimal Front in Multi-Objective Optimization

Clustering Analysis for the Pareto Optimal Front in Multi-Objective Optimization

A Multi-Strategy Marine Predator Algorithm and Its Application in Joint Regularization Semi-Supervised ELM

Quantum Clone Elite Genetic Algorithm‐Based Evaluation Mechanism for Maximizing Network Efficiency in Soil Moisture Wireless Sensor Networks

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