Novel Parallel Heterogeneous Meta-Heuristic and Its Communication Strategies for the Prediction of Wind Power

Pan, Jeng‐Shyang; Hu, Pei; Chu, Shu-Chuan

doi:10.3390/pr7110845

Cited by 84 publications

(34 citation statements)

References 47 publications

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“…In [18], a compact pigeon-inspired optimization algorithm was proposed to solve complex scientific and industrial problems with many data packets, including the use of classical optimization problems and the ability to find optimal solutions in many solution spaces with limited hardware resources. Those studies provide a feasible solution to the problem under acceptable computational time and space, and the solution cannot be predicted in advance [19].…”

Section: 'Pso-infotaxis' Algorithm-based Exploration Of Cooperative Usvsmentioning

confidence: 99%

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Improved ‘Infotaxis’ Algorithm-Based Cooperative Multi-USV Pollution Source Search Approach in Lake Water Environment

Huang

2020

Symmetry

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This paper studies the cooperation method of multi-cooperative Unmanned Surface Vehicles (USVs) for chemical pollution source monitoring in a dynamic water environment. Multiple USVs formed a mobile sensor network in a symmetrical or asymmetrical formation. Based on ‘Infotaxis’ algorithms for multi-USV, an improved shared probability is proposed for solving the problems of low success rate and low efficiency resulting from the cognitive differences of multi-USV in cooperative exploration. By introducing the confidence factor, the cognitive differences between USVs are coordinated. The success rate and the efficiency of exploration are improved. To further optimize the exploration strategy, the particle swarm optimization (PSO) algorithm is introduced into the ‘Infotaxis’ algorithm to plan the USVs’ exploration path. This method is called the ‘PSO-Infotaxis’ algorithm. The effectiveness of the proposed method is verified by simulation and laboratory experiments. A comparison of the test results shows that the ‘PSO-Infotaxis’ algorithm is superior with respect to exploring efficiency. It can reduce the uncertainty of the estimation for source location faster and has lower exploration time, which is most important for the exploration of a large range of water areas.

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Section: 'Pso-infotaxis' Algorithm-based Exploration Of Cooperative Usvsmentioning

confidence: 99%

“…w min = 0.4 c 1 and c 2 are learning factors which represent the experience learned from the particle and the particle group, respectively. The values of c 1 and c 2 are usually 2. r 1 and r 2 are random numbers between 0 and 1 [19].…”

Section: Basic Idea Of Standard Psomentioning

confidence: 99%

Improved ‘Infotaxis’ Algorithm-Based Cooperative Multi-USV Pollution Source Search Approach in Lake Water Environment

Huang

2020

Symmetry

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“…ere are many intelligent optimization algorithms proposed by simulating other phenomena in nature, such as cat swarm algorithm (CSO) [13][14][15], artificial bee colony algorithm (ABC) [16,17], differential evolution algorithm (DE) [18][19][20][21], multiverse optimizer (MVO) [22,23], flower pollination algorithm [24,25], gray wolf algorithm (GWO) [26][27][28], pigeon-inspired algorithm (PIO)…”

Section: Introductionmentioning

confidence: 99%

Sine Cosine Algorithm with Multigroup and Multistrategy for Solving CVRP

Yang

Chu

Pan

et al. 2020

Mathematical Problems in Engineering

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Sine Cosine Algorithm (SCA) has been proved to be superior to some existing traditional optimization algorithms owing to its unique optimization principle. However, there are still disadvantages such as low solution accuracy and poor global search ability. Aiming at the shortcomings of the sine cosine algorithm, a multigroup multistrategy SCA algorithm (MMSCA) is proposed in this paper. e algorithm executes multiple populations in parallel, and each population executes a different optimization strategy. Information is exchanged among populations through intergenerational communication. Using 19 different types of test functions, the optimization performance of the algorithm is tested. Numerical experimental results show that the performance of the MMSCA algorithm is better than that of the original SCA algorithm, and it also has some advantages over other intelligent algorithms. At last, it is applied to solving the capacitated vehicle routing problem (CVRP) in transportation. e algorithm can get better results, and the practicability and feasibility of the algorithm are also proved. [29,30], bat algorithm (BA) [31,32], symbiotic organism search algorithm (SOS) [33,34], and QUATRE [35][36][37].At present, there are so many optimization algorithms, which can solve some complex optimization problems well. Why do we need so many optimization algorithms? As demonstrated by the No Free Lunch (NFL) [38] proposed by Wolpert and Macready, no single optimization algorithm is applicable to all problems. Inspired by this, a new intelligent optimization algorithm was proposed by Australian scholar Mirjalili in 2016, which is called Sine Cosine Algorithm (SCA) [39]. e SCA algorithm iterates through the properties of the sine and cosine functions to achieve optimization. It has fewer parameter settings, is easy to implement, and has a strong optimization ability. It has been proved that it is better than PSO algorithm, genetic algorithm (GA), and firefly algorithm (FA) in convergence with accuracy and speed [39].With the rapid development of software and hardware, parallel computing has become a form of high-performance computing. In evolutionary computing, parallelism often represents the iterative updating of multiple populations at the same time. e advantage of this method is to ensure population diversity, to further improve the search ability and performance of the algorithm. Especially when solving complex optimization problems, parallelizing the algorithm is an effective way to improve the efficiency and accuracy of the algorithm. At present, many existing algorithms have successfully applied the parallel mechanism, such as parallel PSO [40], parallel ACO [41], and parallel QUATRE [42]. Inspired by this, this paper introduces the multigroup and multistrategy optimization mechanism to further improve the SCA algorithm. It is called MMSCA. When the algorithm is solved, multiple populations execute in parallel, and each population adopts different updating strategies. By comparing the results of test functions, MMSCA is be...

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“…Although the objective function has multiple constraints in the objective world, the performance of the objective function of the algorithm under unconstrained conditions plays a fundamental role in various optimization applications, which will then affect the performance and direction of the algorithm in a constrained objective function. Computational intelligence (CI) [6][7][8][9][10] not only deals with complex problems in real life (most notably the objective functions of uncertain or noisy problems [11]), it can also give calculation methods and solutions to solve these optimization problems. Evolutionary computation (EC) [12][13][14][15] is a branch of CI that provides an optimization method with evolutionary ideas.…”

Section: Introductionmentioning

confidence: 99%

Symbiotic Organism Search Algorithm with Multi-Group Quantum-Behavior Communication Scheme Applied in Wireless Sensor Networks

Chu

Pan

2020

Applied Sciences

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The symbiotic organism search (SOS) algorithm is a promising meta-heuristic evolutionary algorithm. Its excellent quality of global optimization solution has aroused the interest of many researchers. In this work, we not only applied the strategy of multi-group communication and quantum behavior to the SOS algorithm, but also formed a novel global optimization algorithm called the MQSOS algorithm. It has speed and convergence ability and plays a good role in solving practical problems with multiple arguments. We also compared MQSOS with other intelligent algorithms under the CEC2013 large-scale optimization test suite, such as particle swarm optimization (PSO), parallel PSO (PPSO), adaptive PSO (APSO), QUasi-Affine TRansformation Evolutionary (QUATRE), and oppositional SOS (OSOS). The experimental results show that MQSOS algorithm had better performance than the other intelligent algorithms. In addition, we combined and optimized the DV-hop algorithm for node localization in wireless sensor networks, and also improved the DV-hop localization algorithm to achieve higher localization accuracy than some existing algorithms.

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Novel Parallel Heterogeneous Meta-Heuristic and Its Communication Strategies for the Prediction of Wind Power

Cited by 84 publications

References 47 publications

Improved ‘Infotaxis’ Algorithm-Based Cooperative Multi-USV Pollution Source Search Approach in Lake Water Environment

Improved ‘Infotaxis’ Algorithm-Based Cooperative Multi-USV Pollution Source Search Approach in Lake Water Environment

Sine Cosine Algorithm with Multigroup and Multistrategy for Solving CVRP

Symbiotic Organism Search Algorithm with Multi-Group Quantum-Behavior Communication Scheme Applied in Wireless Sensor Networks

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