An Elitist Transposon Quantum‐Based Particle Swarm Optimization Algorithm for Economic Dispatch Problems

Wu, A.; Yang, Zhenlun

doi:10.1155/2018/7276585

Cited by 7 publications

(8 citation statements)

References 63 publications

(73 reference statements)

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“…e value of c is getting smaller as t increases in equation (10) (see Figure 2(a)). From equation (10), in the earlier search phase, the value of c is larger, so y is mostly affected by the offspring y 1 ; the difference of x 1 and x 2 is larger, and the search range around x 1 is larger, so the algorithm has stronger exploration. In the later search phase, the value of c is smaller, so y is mostly affected by the offspring y 2 ; the difference between x 1 and x 2 is smaller, the search range around x 2 (good position) is smaller, and the algorithm has stronger exploitation.…”

Section: Laplacian Biogeography-based Optimization Garg Andmentioning

confidence: 99%

“…ere are many parameters to be set and much complexity in equation 10, so a new dynamic weight parameter c is adopted. It is expressed as equation 14, and the difference of c in equations (10) and 14is shown in Figure 2. Figure 2, c is getting smaller as t increases, and it keeps an almost constant value (0.1) after about 100 iterations in LxBBO.…”

Section: Improved Laplace Operatormentioning

confidence: 99%

“…However, they cannot find the optimal solution on other problems or within a reasonable time. In recent decades, inspired by nature, many Intelligent Optimization Algorithms (IOAs) such as Particle Swarm Optimization (PSO) [1], Shuffled Frog Leaping Algorithm (SFLA) [2], Differential Evolution (DE) [3], Krill Herd (KH) [4], Harmony Search (HS) [5], Cuckoo Search (CS) [6], Grey Wolf Optimizer (GWO) [7], and Biogeography-Based Optimization (BBO) [8], have sprung up, and they are widely used in many areas [6,[9][10][11].…”

Section: Introductionmentioning

confidence: 99%

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Improved Laplacian Biogeography-Based Optimization Algorithm and Its Application to QAP

et al. 2020

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Laplacian Biogeography-Based Optimization (LxBBO) is a BBO variant which improves BBO’s performance largely. When it solves some complex problems, however, it has some drawbacks such as poor performance, weak operability, and high complexity, so an improved LxBBO (ILxBBO) is proposed. First, a two-global-best guiding operator is created for guiding the worst habitat mainly to enhance the exploitation of LxBBO. Second, a dynamic two-differential perturbing operator is proposed for the first two best habitats’ updating to improve the global search ability in the early search phase and the local one in the late search one, respectively. Third, an improved Laplace migration operator is formulated for other habitats’ updating to improve the search ability and the operability. Finally, some measures such as example learning, mutation operation removing, and greedy selection are adopted mostly to reduce the computation complexity of LxBBO. A lot of experimental results on the complex functions from the CEC-2013 test set show ILxBBO obtains better performance than LxBBO and quite a few state-of-the-art algorithms do. Also, the results on Quadratic Assignment Problems (QAPs) show that ILxBBO is more competitive compared with LxBBO, Improved Particle Swarm Optimization (IPSO), and Improved Firefly Algorithm (IFA).

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Section: Laplacian Biogeography-based Optimization Garg Andmentioning

confidence: 99%

Section: Improved Laplace Operatormentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Improved Laplacian Biogeography-Based Optimization Algorithm and Its Application to QAP

et al. 2020

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“…e algorithms have global optimization ability and overcome the shortcomings of many traditional path planning algorithms. As a method to solve optimization problems, PSO has been developed rapidly in recent years and widely used in many fields, such as combinatorial optimization [23], scheduling problems [24], and neural networks [25].…”

Section: Introductionmentioning

confidence: 99%

Local Path Planning for Unmanned Surface Vehicle Collision Avoidance Based on Modified Quantum Particle Swarm Optimization

et al. 2020

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An unmanned surface vehicle (USV) plans its global path before the mission starts. When dynamic obstacles appear during sailing, the planned global path must be adjusted locally to avoid collision. This study proposes a local path planning algorithm based on the velocity obstacle (VO) method and modified quantum particle swarm optimization (MQPSO) for USV collision avoidance. The collision avoidance model based on VO not only considers the velocity and course of the USV but also handles the variable velocity and course of an obstacle. According to the collision avoidance model, the USV needs to adjust its velocity and course simultaneously to avoid collision. Due to the kinematic constraints of the USV, the velocity window and course window of the USV are determined by the dynamic window approach (DWA). In summary, local path planning is transformed into a multiobjective optimization problem with multiple constraints in a continuous search space. The optimization problem is to obtain the USV’s optimal velocity variation and course variation to avoid collision and minimize its energy consumption under the rules of the International Regulations for Preventing Collisions at Sea (COLREGs) and the kinematic constraints of the USV. Since USV local path planning is completed in a short time, it is essential that the optimization algorithm can quickly obtain the optimal value. MQPSO is primarily proposed to meet that requirement. In MQPSO, the efficiency of quantum encoding in quantum computing and the optimization ability of representing the motion states of the particles with wave functions to cover the whole feasible solution space are combined. Simulation results show that the proposed algorithm can obtain the optimal values of the benchmark functions and effectively plan a collision-free path for a USV.

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“…However, the PSO often faces premature convergence problem, especially in multimodal problems as it may get stuck in specific point [6]. Wu and Yang [7] presented an elitist transposon quantum-based PSO to solve economic dispatch problems. Eusuff and Lansey [8] presented a shuffled frog-leaping algorithm (SFLA), which is inspired from the memetic evolution of frogs seeking food in a pond.…”

Section: Introductionmentioning

confidence: 99%

Improved Monarch Butterfly Optimization Algorithm Based on Opposition‐Based Learning and Random Local Perturbation

Sun

Chen

et al. 2019

Complexity

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Many optimization problems have become increasingly complex, which promotes researches on the improvement of different optimization algorithms. The monarch butterfly optimization (MBO) algorithm has proven to be an effective tool to solve various kinds of optimization problems. However, in the basic MBO algorithm, the search strategy easily falls into local optima, causing premature convergence and poor performance on many complex optimization problems. To solve the issues, this paper develops a novel MBO algorithm based on opposition-based learning (OBL) and random local perturbation (RLP). Firstly, the OBL method is introduced to generate the opposition-based population coming from the original population. By comparing the opposition-based population with the original population, the better individuals are selected and pass to the next generation, and then this process can efficiently prevent the MBO from falling into a local optimum. Secondly, a new RLP is defined and introduced to improve the migration operator. This operation shares the information of excellent individuals and is helpful for guiding some poor individuals toward the optimal solution. A greedy strategy is employed to replace the elitist strategy to eliminate setting the elitist parameter in the basic MBO, and it can reduce a sorting operation and enhance the computational efficiency. Finally, an OBL and RLP-based improved MBO (OPMBO) algorithm with its complexity analysis is developed, following on which many experiments on a series of different dimensional benchmark functions are performed and the OPMBO is applied to clustering optimization on several public data sets. Experimental results demonstrate that the proposed algorithm can achieve the great optimization performance compared with a few state-of-the-art algorithms in most of the test cases.

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An Elitist Transposon Quantum‐Based Particle Swarm Optimization Algorithm for Economic Dispatch Problems

Cited by 7 publications

References 63 publications

Improved Laplacian Biogeography-Based Optimization Algorithm and Its Application to QAP

Improved Laplacian Biogeography-Based Optimization Algorithm and Its Application to QAP

Local Path Planning for Unmanned Surface Vehicle Collision Avoidance Based on Modified Quantum Particle Swarm Optimization

Improved Monarch Butterfly Optimization Algorithm Based on Opposition‐Based Learning and Random Local Perturbation

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