scholarly journals THE EFFICIENCY ANALYSIS OF MULTI-AGENT OPTIMIZATION METHODS OF AIRCRAFT DESIGNS ELEMENTS

2019 ◽  
Vol 22 (2) ◽  
pp. 96-108
Author(s):  
A. V. Panteleev ◽  
M.M. S. Karane
Keyword(s):  
Optimization Problems ◽  
Optimization Methods ◽  
Metaheuristic Algorithms ◽  
Great Success ◽  
Structural Elements ◽  
Development Environment ◽  
Fish School ◽  
Krill Herd ◽  
Multi Agent ◽  
The Given

The article considers the use of three multi-agent methods for optimizing structural elements of aircraft. The research describes strategies for finding solutions to multi-agent metaheuristic algorithms, such as: fish school search, krill herd, and imperialist competition algorithm. The work of these methods is based on the processes occurring in an environment that features many agents. Agents have the opportunity to exchange information in order to find a solution to the problem. These methods allow you to find an approximate solution, but, nevertheless, with great success are used in practice. In this regard, the described metaheuristic algorithms were applied to the optimization problems of structural elements of aircraft such as: welded beam, high pressure vessel, gearbox and tension spring. The article adduces the formulation of these problems: the objective function, a set of constraints and a set of admissible solutions are indicated, recommendations on the choice of parameters of the methods used are given. To solve the problems of optimizing the elements of aircraft construction, a set of software elements was formed in the development environment of Microsoft Visual Studio in C #. This complex of programs allows you to solve the given problems by each of the described multi-agent methods. The software allows you to select a method, a task and select the method parameters and the penalty function coefficients in the best possible way. The results of the solution were compared with each other and with the well- known solution. According to the numerical results of solving these tasks, we can conclude that the algorithmic and software created allow us to find a solution close to the exact one in a reasonable time.

scholarly journals Simulated Annealing-Based Krill Herd Algorithm for Global Optimization

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Gai-Ge Wang ◽  
Lihong Guo ◽  
Amir Hossein Gandomi ◽  
Amir Hossein Alavi ◽  
Hong Duan
Keyword(s):  
Global Optimization ◽  
Simulated Annealing ◽  
Optimization Problems ◽  
Optimization Methods ◽  
Greedy Strategy ◽  
Krill Herd Algorithm ◽  
Krill Herd ◽  
Low Probability ◽  
Elitism Scheme ◽  
Reliability And Robustness

Recently, Gandomi and Alavi proposed a novel swarm intelligent method, called krill herd (KH), for global optimization. To enhance the performance of the KH method, in this paper, a new improved meta-heuristic simulated annealing-based krill herd (SKH) method is proposed for optimization tasks. A new krill selecting (KS) operator is used to refine krill behavior when updating krill’s position so as to enhance its reliability and robustness dealing with optimization problems. The introduced KS operator involves greedy strategy and accepting few not-so-good solutions with a low probability originally used in simulated annealing (SA). In addition, a kind of elitism scheme is used to save the best individuals in the population in the process of the krill updating. The merits of these improvements are verified by fourteen standard benchmarking functions and experimental results show that, in most cases, the performance of this improved meta-heuristic SKH method is superior to, or at least highly competitive with, the standard KH and other optimization methods.

scholarly journals A Grey Wolf Optimizer Feature Selection method and its Effect on the Performance of Document Classification Problem

2021 ◽  
Vol 4 (2) ◽  
pp. 116-122
Author(s):  
Ibraheem Al-Jadir ◽  
Waleed A. Mahmoud
Keyword(s):  
Feature Selection ◽  
Feature Selection Method ◽  
Optimization Methods ◽  
Classification Problem ◽  
Performance Outcomes ◽  
Grey Wolf Optimizer ◽  
Great Success ◽  
Grey Wolf ◽  
Feature Selection Problem ◽  
Krill Herd

Optimization methods are considered as one of the highly developed areas in Artificial Intelligence (AI). The success of the Particle Swarm Optimization (PSO) and Genetic Algorithms (GA) has encouraged researchers to develop other methods that can obtain better performance outcomes and to be more responding to the modern needs. The Grey Wolf Optimization (GWO), and the Krill Herd (KH) are some of those methods that showed a great success in different applications in the last few years. In this paper, we propose a comparative study of using different optimization methods including KH and GWO in order to solve the problem of document feature selection for the classification problem. These methods are used to model the feature selection problem as a typical optimization method. Due to the complexity and the non-linearity of this kind of problems, it becomes necessary to use some advanced techniques to make the judgement of which features subset that is optimal to enhance the performance of classification of text documents. The test results showed the superiority of GWO over the other counterparts using the specified evaluation measures.

scholarly journals Research and Study of the Hybrid Algorithms Based on the Collective Behavior of Fish Schools and Classical Optimization Methods

Algorithms ◽  
2020 ◽  
Vol 13 (4) ◽  
pp. 85 ◽  
Author(s):  
Liliya A. Demidova ◽  
Artyom V. Gorchakov
Keyword(s):  
Collective Behavior ◽  
Hybrid Algorithm ◽  
Gradient Descent ◽  
Optimization Problems ◽  
Computation Time ◽  
Optimization Methods ◽  
Hybrid Algorithms ◽  
Fish Schools ◽  
Fish School ◽  
Classical Optimization

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.

scholarly journals Lévy-Flight Krill Herd Algorithm

2013 ◽  
Vol 2013 ◽  
pp. 1-14 ◽  
Author(s):  
Gaige Wang ◽  
Lihong Guo ◽  
Amir Hossein Gandomi ◽  
Lihua Cao ◽  
Amir Hossein Alavi ◽  
...  
Keyword(s):  
Optimization Problems ◽  
Computing Time ◽  
Optimization Methods ◽  
Population Based ◽  
Global Optimum ◽  
Lévy Flight ◽  
Levy Flight ◽  
Krill Herd Algorithm ◽  
Krill Herd ◽  
Elitism Scheme

To improve the performance of the krill herd (KH) algorithm, in this paper, a Lévy-flight krill herd (LKH) algorithm is proposed for solving optimization tasks within limited computing time. The improvement includes the addition of a new local Lévy-flight (LLF) operator during the process when updating krill in order to improve its efficiency and reliability coping with global numerical optimization problems. The LLF operator encourages the exploitation and makes the krill individuals search the space carefully at the end of the search. The elitism scheme is also applied to keep the best krill during the process when updating the krill. Fourteen standard benchmark functions are used to verify the effects of these improvements and it is illustrated that, in most cases, the performance of this novel metaheuristic LKH method is superior to, or at least highly competitive with, the standard KH and other population-based optimization methods. Especially, this new method can accelerate the global convergence speed to the true global optimum while preserving the main feature of the basic KH.

scholarly journals Optimisation of production machine scheduling using a two level mixed optimisation method

2010 ◽  
Vol 20 (2) ◽  
pp. 197-212
Author(s):  
Rana Anil ◽  
Ajit Verma ◽  
A.S. Srividya
Keyword(s):  
Optimization Problems ◽  
Machine Scheduling ◽  
Optimization Methods ◽  
Optimization Method ◽  
Search Optimization ◽  
Maximum Benefit ◽  
Production Manager ◽  
Mixed Optimization ◽  
Machining Operations ◽  
The Given

This paper presents an application of a two level mixed optimization method on a machine scheduling problem of a government owned machine shop. Where evolutionary algorithm methods are suitable for solving complex, discrete space, and non-linear, discontinuous optimization problems; classical direct-search optimization methods are suitable and efficient in handling simple unimodal problems requiring less computation. Both methods are used at two levels, the first level decides which machines to be used for the machining operations and how much overtime (at extra cost) to be allotted to each work order, the second level decides for which operation and on which day the overtime should be allotted so as to attain its maximum benefit. A sample problem has been solved by using the above methods and a range of non-dominated solutions have been presented in a tabular form to enable the production manager to choose his options based on the given criticality of the work order.

A Multi-Stage Krill Herd Algorithm for Global Numerical Optimization

2016 ◽  
Vol 25 (02) ◽  
pp. 1550030 ◽  
Author(s):  
Gai-Ge Wang ◽  
Amir H. Gandomi ◽  
Amir H. Alavi ◽  
Suash Deb
Keyword(s):  
Numerical Optimization ◽  
Optimization Problems ◽  
Optimization Methods ◽  
Optimization Method ◽  
Fine Tuning ◽  
Benchmark Problems ◽  
Global Numerical Optimization ◽  
Candidate Solution ◽  
Multi Stage ◽  
Krill Herd

A multi-stage krill herd (MSKH) algorithm is presented to fully exploit the global and local search abilities of the standard krill herd (KH) optimization method. The proposed method involves exploration and exploitation stages. The exploration stage uses the basic KH algorithm to select a good candidate solution set. This phase is followed by fine-tuning a good candidate solution in the exploitation stage with a focused local mutation and crossover (LMC) operator in order to enhance the reliability of the method for solving global numerical optimization problems. Moreover, the elitism scheme is introduced into the MSKH method to guarantee the best solution. The performance of MSKH is verified using twenty-five standard and rotated and shifted benchmark problems. The results show the superiority of the proposed algorithm to the standard KH and other well-known optimization methods.

scholarly journals Behavior Selection Metaheuristic Search Algorithm for the Pollination Optimization: A Simulation Case of Cocoa Flowers

Algorithms ◽  
2021 ◽  
Vol 14 (8) ◽  
pp. 230
Author(s):  
Willa Ariela Syafruddin ◽  
Rio Mukhtarom Paweroi ◽  
Mario Köppen
Keyword(s):  
Optimization Problems ◽  
Search Algorithm ◽  
Random Search ◽  
Optimization Methods ◽  
Multi Agent Systems ◽  
Real World Problem ◽  
Multi Agent ◽  
Flower Type ◽  
Excellent Source

Since nature is an excellent source of inspiration for optimization methods, many optimization algorithms have been proposed, are inspired by nature, and are modified to solve various optimization problems. This paper uses metaheuristics in a new field inspired by nature; more precisely, we use pollination optimization in cocoa plants. The cocoa plant was chosen as the object since its flower type differs from other kinds of flowers, for example, by using cross-pollination. This complex relationship between plants and pollinators also renders pollination a real-world problem for chocolate production. Therefore, this study first identified the underlying optimization problem as a deferred fitness problem, where the quality of a potential solution cannot be immediately determined. Then, the study investigates how metaheuristic algorithms derived from three well-known techniques perform when applied to the flower pollination problem. The three techniques examined here are Swarm Intelligence Algorithms, Individual Random Search, and Multi-Agent Systems search. We then compare the behavior of these various search methods based on the results of pollination simulations. The criteria are the number of pollinated flowers for the trees and the amount and fairness of nectar pickup for the pollinator. Our results show that Multi-Agent System performs notably better than other methods. The result of this study are insights into the co-evolution of behaviors for the collaborative pollination task. We also foresee that this investigation can also help farmers increase chocolate production by developing methods to attract and promote pollinators.

scholarly journals A New Multi-Agent Approach for Solving Optimization Problems with High-Dimensional: Case Study in Email Spam Detection

2020 ◽  
Author(s):  
Hekmat Mohmmadzadeh ◽  
Farhad Soleimanian Gharehchopogh
Keyword(s):  
Optimization Problems ◽  
Population Diversity ◽  
Metaheuristic Algorithms ◽  
High Dimensional ◽  
Flower Pollination Algorithm ◽  
Multi Agent Systems ◽  
Agent Systems ◽  
Whale Optimization ◽  
Multi Agent ◽  
The Common

There exist numerous high-dimensional problems in the real world which cannot be solved through the common traditional methods. The metaheuristic algorithms have been developed as successful techniques for solving a variety of complex and difficult optimization problems. Notwithstanding their advantages, these algorithms may turn out to have weak points such as lower population diversity and lower convergence rate when facing complex high-dimensional problems. An appropriate approach to solve such problems is to apply multi-agent systems along with the metaheuristic algorithms. The present paper proposes a new approach based on the multi-agent systems and the concept of agent, which is named Multi-Agent Metaheuristic (MAMH) method. In the proposed approach, several basic and powerful metaheuristic algorithms, including Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), Firefly Algorithm (FA), Bat Algorithm (BA), Flower Pollination Algorithm (FPA), Gray Wolf Optimizer (GWO), Whale Optimization Algorithm (WOA), Crow Search Algorithm (CSA), Farmland Fertility Algorithm (FFA), are considered as separate agents each of which sought to achieve its own goals while competing and cooperating with others to achieve the common goals. In overall, the proposed method was tested on 32 complex benchmark functions, the results of which indicated effectiveness and powerfulness of the proposed method for solving the high-dimensional optimization problems. In addition, in this paper, the binary version of the proposed approach, called Binary MAMH (BMAMH), was executed on the spam email dataset. According to the results, the proposed method exhibited a higher precision in detection of the spam emails compared to other metaheuristic algorithms and methods.

Grey Optimization Problems Using Prey-Predator Algorithm

2018 ◽  
pp. 31-46
Author(s):  
Nawaf N. Hamadneh
Keyword(s):  
Optimization Problems ◽  
System Modeling ◽  
Optimization Methods ◽  
Metaheuristic Algorithms ◽  
Grey System ◽  
Solution Methods ◽  
Grey Systems Theory ◽  
Deterministic Solution ◽  
Best Parameter ◽  
Grey Number

The optimization problems are the problem of finding the best parameter values which optimize the objective functions. The optimization methods are divided into two types: deterministic and non-deterministic methods. Metaheuristic algorithms fall in the non-deterministic solution methods. Prey-predator algorithm is one of the well-known metaheuristic algorithms developed for optimization problems. It has gained popularity within a short time and is used in different applications, and it is an easy algorithm to understand and also to implement. The grey systems theory was initialized as uncertain systems. Each grey system is described with grey numbers, grey equations, and grey matrices. A grey number has uncertain value, but there is an interval or a general set of numbers, within that the value lies is known. In this chapter, the author will review and show that grey system modeling is very useful to use with prey-predator algorithm. The benchmark functions, grey linear programming, and grey model GM (1,1) are used as examples of grey system.

scholarly journals CHAOS SYNCHRONIZATION: A LAGRANGE PROGRAMMING NETWORK APPROACH

2000 ◽  
Vol 10 (04) ◽  
pp. 797-810 ◽  
Author(s):  
J. A. K. SUYKENS ◽  
J. VANDEWALLE
Keyword(s):  
Chaos Synchronization ◽  
Optimization Problems ◽  
Optimization Methods ◽  
Problem Formulation ◽  
Lorenz Attractor ◽  
Synchronization Errors ◽  
Time Optimization ◽  
Improved Performance ◽  
Hard And Soft Constraints ◽  
The Given

In this paper we interpret chaos synchronization schemes within the framework of Lagrange programming networks, which form a class of continuous-time optimization methods for solving constrained nonlinear optimization problems. From this study it follows that standard synchronization schemes can be regarded as a Lagrange programming network with soft constraining, where synchronization between state vectors is defined as a constraint to the dynamical systems. New schemes are proposed then which implement synchronization by hard and soft constraints within Lagrange programming networks. A version is derived which takes into account synchronization errors within the problem formulation. Furthermore Lagrange programming networks for achieving partial and generalized synchronization are given. The methods assume the existence of potential functions for the given systems. The proposed Lagrange programming networks with hard and soft constraining show improved performance on many simulation examples for identical and nonidentical chaotic systems. The schemes are illustrated on Chua's circuit, Lorenz attractor and n-scroll circuits.

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