While the single ant colony algorithm and the fish swarm algorithm have many advantages, they also have various shortcomings. After analyzing the advantages and disadvantages of the ant colony algorithm and the fish s...
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While the single ant colony algorithm and the fish swarm algorithm have many advantages, they also have various shortcomings. After analyzing the advantages and disadvantages of the ant colony algorithm and the fish swarm algorithm, this paper uses the complementary principle of the two algorithms to effectively fuse the two population intelligent algorithms. The improved swarm intelligence algorithm is applied to the well-considered protein folding prediction problem, and the simplified protein structure Toy model is verified, and the ideal results are obtained. The improved algorithm enhances the search ability, and the computational efficiency is greatly improved, ensuring the accuracy of the operation.
The noise reduction technology of underwater targets is continuously improved, and the target radiation noise is getting more and more weak. It is very important to find out the effective feature extraction and recogn...
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ISBN:
(纸本)9781538657744
The noise reduction technology of underwater targets is continuously improved, and the target radiation noise is getting more and more weak. It is very important to find out the effective feature extraction and recognition method of target weak radiated noise in the reconnaissance and surveillance of the sea target and the implementation of precision strikes. In this paper, a recognition method based on fractional stochastic resonance is proposed to deal with the problem of weak ship-radiated noise under alpha stable noise distribution. Based on the fractional stochastic resonance algorithm, this method introduces the optimization parameters of artificial-fish swarm algorithm, effectively solves the optimization problem of fractional order, and then uses the power spectrum characteristics as the recognition feature, and uses the support vector machine classifier to classify and identify. Theoretical analysis and simulation experiments verify the effectiveness of the proposed algorithm.
It is a challenge for the distribution grid with more and more PV generation connected. The limit capacity calculation of PV generation is the key for the problems of distribution grid. The paper proposes method for t...
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ISBN:
(纸本)9781538685495
It is a challenge for the distribution grid with more and more PV generation connected. The limit capacity calculation of PV generation is the key for the problems of distribution grid. The paper proposes method for the calculation of limit capacity of PV generation in the distribution grid. The proposed model takes the maximum capacity of PV generation as the objective, and the operation constraints contain bus voltage deviation, line current, main transformer limit, reactive power compensation and power turning. fish swarm algorithm is used for the calculation of the limit capacity of PV generation. The simulation of IEEE33 bus system shows that the proposed method can increase the grid-connected limit capacity of PV generation under the premise of meeting the constraints of distribution grid system. It verifies the rationality and feasibility of the proposed method, and provides a solution for the limit capacity of distributed PV generation connected into distribution grid.
Bio-inspired computing represents the umbrella of different studies of computer science, mathematics, and biology in the last years. Bio-inspired computing optimization algorithms is an emerging approach which is base...
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Bio-inspired computing represents the umbrella of different studies of computer science, mathematics, and biology in the last years. Bio-inspired computing optimization algorithms is an emerging approach which is based on the principles and inspiration of the biological evolution of nature to develop new and robust competing techniques. In the last years, the bio-inspired optimization algorithms are recognized in machine learning to address the optimal solutions of complex problems in science and engineering. However, these problems are usually nonlinear and restricted to multiple nonlinear constraints which propose many problems such as time requirements and high dimensionality to find the optimal solution. To tackle the problems of the traditional optimization algorithms, the recent trends tend to apply bio-inspired optimization algorithms which represent a promising approach for solving complex optimization problems. This paper presents state-of-art of nine of recent bio-inspired algorithms, gap analysis, and its applications namely; Genetic Bee Colony (GBC) algorithm, fish swarm algorithm (FSA), Cat swarm Optimization (CSO), Whale Optimization algorithm (WOA), Artificial Algae algorithm (AAA), Elephant Search algorithm (ESA), Chicken swarm Optimization algorithm (CSOA), Moth flame optimization (MFO), and Grey Wolf Optimization (GWO) algorithm. The previous related works are collected from Scopus databases are presented. Also, we explore some key issues in optimization and some applications for further research. We also analyze in-depth discussions the essence of these algorithms and their connections to self-organization and its applications in different areas of research are presented. As a result, the proposed analysis of these algorithms leads to some key problems that have to be addressed in the future.
The fish swarm algorithm (FSA) is a new intelligent swarm modeling approach that consists primarily of searching, swarming, and following behaviors. This paper proposes several improvements of the FSA, including: (1) ...
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The fish swarm algorithm (FSA) is a new intelligent swarm modeling approach that consists primarily of searching, swarming, and following behaviors. This paper proposes several improvements of the FSA, including: (1) using particle swarm optimization formulation to reformulate the FSA, (2) integrating communication behavior into FSA, and (3) creating formulas for major FSA parameters. This paper also focuses on studying the effects of FSA behaviors on optimization during the evolution process. Results focus on the two case study categories of function optimization (eight benchmark functions) and neural network learning (single-input single-output system identification, multi-inputs single output system identification and Iris classification problem). Evidence indicates that the proposed FSA approach reduces the effort necessary to set parameters and that the proposed communication behavior indeed improves FSA. Crown Copyright (C) 2011 Published by Elsevier B.V. All rights reserved.
To solve VRPTW(the Vehicle Routing Problem with Time Windows), the Genetic with Ant Colony algorithm were mixed as a new algorithm (ACO-GAF),In ant colony state transition probability formula,capacity and time window ...
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ISBN:
(纸本)9781538636756
To solve VRPTW(the Vehicle Routing Problem with Time Windows), the Genetic with Ant Colony algorithm were mixed as a new algorithm (ACO-GAF),In ant colony state transition probability formula,capacity and time window tightness factors were increased in it;In order to jump out of local optimal, the fish operators were introduced after the crossover and mutation operation of Genetic algorithm;Then merged optimization solution group;After calculate the fitness function by roulette wheel selection out of the best individual, after the completion of the optimal path pheromone update. On the MATLAB platform, the use of Solomon RC series numerical example in the database, set appropriate parameter values, to the shortest path and the number of vehicles at least as the goal, and ACO-GAF algorithm to solve the numerical example results compared with the current optimal solution, the results show that the ACO-GAF algorithm made a great progress in reducing vehicle;In addition, comparing the results of ACO-GAF algorithm with Genetic algorithm, Ant Colony algorithm, the fishalgorithm, The ACO-GAF algorithm in the optimization efficiency and optimization results are superior to the single algorithm.
The problem of multi robot task allocation and scheduling is to assign more relative tasks to less relative robots and to scheme task processing sequence so as to minimize the processing time of these tasks. The key o...
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ISBN:
(纸本)9781424467129
The problem of multi robot task allocation and scheduling is to assign more relative tasks to less relative robots and to scheme task processing sequence so as to minimize the processing time of these tasks. The key of this problem is to allocate proper quantity of tasks for each robot and schedule the optimal task sequence for each robot. In order to minimize the processing time for robots, an optimized multiple robots task allocation and scheduling approach based on fish swarm algorithm is proposed. In this approach, the optimized task sequence is first schemed using fish swarm algorithm on the assumption that all the tasks are processed by one robot. Then, according to the number of the robots, the task sequence has been randomly divided into several task segments that will be assigned to robots. At last, the task numbers of each task segments are averaged according to the time each robot used, therefore proper quantity of tasks is allocated to each robot and the optimized task allocation scheme is got. To validate the effectiveness of the proposed approach, experiments and simulation have been made. The results show that the proposed approach can scheme optimized multi robots task allocation and scheduling scheme.
An improved fish-Search algorithm was proposed for airplane route planning of a class of Plane-Missile cooperation. The mathematical description of this class of cooperation was introduced. Comprehensively considered ...
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ISBN:
(纸本)9780769536156
An improved fish-Search algorithm was proposed for airplane route planning of a class of Plane-Missile cooperation. The mathematical description of this class of cooperation was introduced. Comprehensively considered such key factors of the cooperation as inter-visibility, threat, maximum distance and relative orientation, the constraint conditions and evaluation index were constructed. According to the characteristics of the problem, the fish-Search algorithm was used to solve the problem, and was improved by introducing a ta-boo bulletin board and the survival mechanism. As is shown in the comparison of the simulation results of the original and the improved algorithm, the convergence rate was improved.
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