scholarly journals The research and development of the hybrid algorithm based on the collective behavior of Fish schools and the classical optimization methods

Author(s):  
L Demidova ◽  
A Gorchakov
Algorithms ◽  
2020 ◽  
Vol 13 (4) ◽  
pp. 85 ◽  
Author(s):  
Liliya A. Demidova ◽  
Artyom V. Gorchakov

Inspired by biological systems, swarm intelligence algorithms are widely used to solve multimodal optimization problems. In this study, we consider the hybridization problem of an algorithm based on the collective behavior of fish schools. The algorithm is computationally inexpensive compared to other population-based algorithms. Accuracy of fish school search increases with the increase of predefined iteration count, but this also affects computation time required to find a suboptimal solution. We propose two hybrid approaches, intending to improve the evolutionary-inspired algorithm accuracy by using classical optimization methods, such as gradient descent and Newton’s optimization method. The study shows the effectiveness of the proposed hybrid algorithms, and the strong advantage of the hybrid algorithm based on fish school search and gradient descent. We provide a solution for the linearly inseparable exclusive disjunction problem using the developed algorithm and a perceptron with one hidden layer. To demonstrate the effectiveness of the algorithms, we visualize high dimensional loss surfaces near global extreme points. In addition, we apply the distributed version of the most effective hybrid algorithm to the hyperparameter optimization problem of a neural network.


Author(s):  
VINCENT ROBERGE ◽  
MOHAMMED TARBOUCHI ◽  
FRANÇOIS ALLAIRE

In this paper, we present a parallel hybrid metaheuristic that combines the strengths of the particle swarm optimization (PSO) and the genetic algorithm (GA) to produce an improved path-planner algorithm for fixed wing unmanned aerial vehicles (UAVs). The proposed solution uses a multi-objective cost function we developed and generates in real-time feasible and quasi-optimal trajectories in complex 3D environments. Our parallel hybrid algorithm simulates multiple GA populations and PSO swarms in parallel while allowing migration of solutions. This collaboration between the GA and the PSO leads to an algorithm that exhibits the strengths of both optimization methods and produces superior solutions. Moreover, by using the "single-program, multiple-data" parallel programming paradigm, we maximize the use of today's multicore CPU and significantly reduce the execution time of the parallel program compared to a sequential implementation. We observed a quasi-linear speedup of 10.7 times faster on a 12-core shared memory system resulting in an execution time of 5 s which allows in-flight planning. Finally, we show with statistical significance that our parallel hybrid algorithm produces superior trajectories to the parallel GA or the parallel PSO we previously developed.


Author(s):  
E. Parsopoulos Konstantinos ◽  
N. Vrahatis Michael

In the previous chapters, we presented the fundamental concepts and variants of PSO, as along with a multitude of recent research results. The reported results suggest that PSO can be a very useful tool for solving optimization problems from different scientific and technological fields, especially in cases where classical optimization methods perform poorly or their application involves formidable technical difficulties due to the problem’s special structure or nature. PSO was capable of addressing continuous and integer optimization problems, handling noisy and multiobjective cases, and producing efficient hybrid schemes in combination with specialized techniques or other algorithms in order to detect multiple (local or global) minimizers or control its own parameters.


Processes ◽  
2020 ◽  
Vol 8 (8) ◽  
pp. 980
Author(s):  
Gustavo Meirelles ◽  
Bruno Brentan ◽  
Joaquín Izquierdo ◽  
Edevar Luvizotto

Agent-based algorithms, based on the collective behavior of natural social groups, exploit innate swarm intelligence to produce metaheuristic methodologies to explore optimal solutions for diverse processes in systems engineering and other sciences. Especially for complex problems, the processing time, and the chance to achieve a local optimal solution, are drawbacks of these algorithms, and to date, none has proved its superiority. In this paper, an improved swarm optimization technique, named Grand Tour Algorithm (GTA), based on the behavior of a peloton of cyclists, which embodies relevant physical concepts, is introduced and applied to fourteen benchmarking optimization problems to evaluate its performance in comparison to four other popular classical optimization metaheuristic algorithms. These problems are tackled initially, for comparison purposes, with 1000 variables. Then, they are confronted with up to 20,000 variables, a really large number, inspired in the human genome. The obtained results show that GTA clearly outperforms the other algorithms. To strengthen GTA’s value, various sensitivity analyses are performed to verify the minimal influence of the initial parameters on efficiency. It is demonstrated that the GTA fulfils the fundamental requirements of an optimization algorithm such as ease of implementation, speed of convergence, and reliability. Since optimization permeates modeling and simulation, we finally propose that GTA will be appealing for the agent-based community, and of great help for a wide variety of agent-based applications.


2020 ◽  
Vol 14 (3) ◽  
pp. 375-380
Author(s):  
Piotr Gołębiowski ◽  
Jolanta Żak ◽  
Piotr Kisielewski

Public transport plays an increasingly important role in satisfying the transport needs. Travellers’ requirements regarding the quality of services are increasing. In addition to passenger comfort, other parameters are important (timetable and the state of transport infrastructure). Therefore, methods that determine the appropriate organization of public transport for an area should be sought. The purpose of the article is to present the most commonly used optimization methods and tools that have been applied to the chosen problems of organization of public transport mainly in Poland (described in the articles of mainly Polish scientists), but against the background of global research. The article characterizes the functioning of public transport in Poland. The selected problems of public transport functioning, which can be solved by using optimization methods and tools were discussed. The chosen methods that were used to formulate and solve the identified problems were indicated. The effects of this article will form part of the work on the POIR.01.01.01-00-0970/17-00 project "IT system for computer-aided public transport planning" financed by the National Centre for Research and Development.


2012 ◽  
Vol 17 (4) ◽  
pp. 307-312
Author(s):  
Piotr Urbanek ◽  
Jacek Kucharski ◽  
Andrzej Frączyk

Abstract In many technical applications it is necessary to know thermal diffusity and heat conduction in solids, liquids and gaseous. In case of solid bodies the experimental determination of those properties requires use of special laboratory stands which provides the appropriate initial and boundary conditions necessary to solve invert problems. In the paper two methods of determine thermal property of metals has been presented and discussed. First method is based on classical optimization methods and the second one by artificial neural network, which is trained with data from numerical model of investigated body. Both method were tested on real laboratory model.


2021 ◽  
Vol 33 (3) ◽  
pp. 547-555
Author(s):  
Hitoshi Habe ◽  
Yoshiki Takeuchi ◽  
Kei Terayama ◽  
Masa-aki Sakagami ◽  
◽  
...  

We propose a pose estimation method using a National Advisory Committee for Aeronautics (NACA) airfoil model for fish schools. This method allows one to understand the state in which fish are swimming based on their posture and dynamic variations. Moreover, their collective behavior can be understood based on their posture changes. Therefore, fish pose is a crucial indicator for collective behavior analysis. We use the NACA model to represent the fish posture; this enables more accurate tracking and movement prediction owing to the capability of the model in describing posture dynamics. To fit the model to video data, we first adopt the DeepLabCut toolbox to detect body parts (i.e., head, center, and tail fin) in an image sequence. Subsequently, we apply a particle filter to fit a set of parameters from the NACA model. The results from DeepLabCut, i.e., three points on a fish body, are used to adjust the components of the state vector. This enables more reliable estimation results to be obtained when the speed and direction of the fish change abruptly. Experimental results using both simulation data and real video data demonstrate that the proposed method provides good results, including when rapid changes occur in the swimming direction.


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