Management applications and other classical optimization problems

Afterword

Particle Swarm Optimization and Intelligence - Advances in Computational Intelligence and Robotics ◽

10.4018/978-1-61520-666-7.ch012 ◽

2010 ◽

pp. 269-272

Author(s):

E. Parsopoulos Konstantinos ◽

N. Vrahatis Michael

Keyword(s):

Optimization Problems ◽

Optimization Methods ◽

Special Structure ◽

Integer Optimization ◽

Hybrid Schemes ◽

Research Results ◽

Global Minimizers ◽

Classical Optimization

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.

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Grand Tour Algorithm: Novel Swarm-Based Optimization for High-Dimensional Problems

Processes ◽

10.3390/pr8080980 ◽

2020 ◽

Vol 8 (8) ◽

pp. 980

Author(s):

Gustavo Meirelles ◽

Bruno Brentan ◽

Joaquín Izquierdo ◽

Edevar Luvizotto

Keyword(s):

Systems Engineering ◽

Collective Behavior ◽

Optimization Problems ◽

Optimization Technique ◽

Optimal Solution ◽

Sensitivity Analyses ◽

Grand Tour ◽

Agent Based ◽

Local Optimal Solution ◽

Classical Optimization

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.

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Fuzzy Adaptive Genetic Algorithm for Improving the Solution of Industrial Optimization Problems

Journal of Intelligent Systems ◽

10.1515/jisys-2016-0343 ◽

2018 ◽

Vol 29 (1) ◽

pp. 409-422 ◽

Cited By ~ 1

Author(s):

Marco Vannucci ◽

Valentina Colla ◽

Stefano Dettori ◽

Vincenzo Iannino

Keyword(s):

Genetic Algorithm ◽

Fuzzy Inference ◽

Optimization Problems ◽

Industrial Applications ◽

Adaptive Genetic Algorithm ◽

Inference System ◽

Complex Models ◽

Fuzzy Adaptive ◽

Classical Optimization

Abstract In the industrial and manufacturing fields, many problems require tuning of the parameters of complex models by means of exploitation of empirical data. In some cases, the use of analytical methods for the determination of such parameters is not applicable; thus, heuristic methods are employed. One of the main disadvantages of these approaches is the risk of converging to “suboptimal” solutions. In this article, the use of a novel type of genetic algorithm is proposed to overcome this drawback. This approach exploits a fuzzy inference system that controls the search strategies of genetic algorithm on the basis of the real-time status of the optimization process. In this article, this method is tested on classical optimization problems and on three industrial applications that put into evidence the improvement of the capability of avoiding the local minima and the acceleration of the search process.

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Improved Particle Swarm Optimization Algorithm Based on Last-Eliminated Principle and Enhanced Information Sharing

Computational Intelligence and Neuroscience ◽

10.1155/2018/5025672 ◽

2018 ◽

Vol 2018 ◽

pp. 1-17 ◽

Cited By ~ 3

Author(s):

Xueying Lv ◽

Yitian Wang ◽

Junyi Deng ◽

Guanyu Zhang ◽

Liu Zhang

Keyword(s):

Particle Swarm Optimization ◽

Information Sharing ◽

Optimization Problems ◽

Optimization Algorithms ◽

Particle Swarm ◽

Population Diversity ◽

Search Performance ◽

Information Communication ◽

Swarm Optimization ◽

Classical Optimization

In this study, an improved eliminate particle swarm optimization (IEPSO) is proposed on the basis of the last-eliminated principle to solve optimization problems in engineering design. During optimization, the IEPSO enhances information communication among populations and maintains population diversity to overcome the limitations of classical optimization algorithms in solving multiparameter, strong coupling, and nonlinear engineering optimization problems. These limitations include advanced convergence and the tendency to easily fall into local optimization. The parameters involved in the imported “local-global information sharing” term are analyzed, and the principle of parameter selection for performance is determined. The performances of the IEPSO and classical optimization algorithms are then tested by using multiple sets of classical functions to verify the global search performance of the IEPSO. The simulation test results and those of the improved classical optimization algorithms are compared and analyzed to verify the advanced performance of the IEPSO algorithm.

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

Algorithms ◽

10.3390/a13040085 ◽

2020 ◽

Vol 13 (4) ◽

pp. 85 ◽

Cited By ~ 2

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.

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Adaptive Plant Propagation Algorithm for Solving Economic Load Dispatch Problem

10.31224/osf.io/sh8p4 ◽

2017 ◽

Cited By ~ 1

Author(s):

Sayan Nag

Keyword(s):

Optimization Problems ◽

State Of The Art ◽

Economic Load Dispatch ◽

Objective Functions ◽

Plant Propagation ◽

Propagation Algorithm ◽

Highly Nonlinear ◽

Plant Intelligence ◽

Classical Optimization

Optimization problems in design engineering are complex by nature, often because of the involvement of critical objective functions accompanied by a number of rigid constraints associated with the products involved. One such problem is Economic Load Dispatch (ED) problem which focuses on the optimization of the fuel cost while satisfying some system constraints. Classical optimization algorithms are not sufficient and also inefficient for the ED problem involving highly nonlinear, and non-convex functions both in the objective and in the constraints. This led to the development of metaheuristic optimization approaches which can solve the ED problem almost efficiently. This paper presents a novel robust plant intelligence based Adaptive Plant Propagation Algorithm (APPA) which is used to solve the classical ED problem. The application of the proposed method to the 3-generator and 6-generator systems shows the efficiency and robustness of the proposed algorithm. A comparative study with another state-of-the-art algorithm (APSO) demonstrates the quality of the solution achieved by the proposed method along with the convergence characteristics of the proposed approach.

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Two Classical Optimization Problems

Shortest Connectivity - Combinatorial Optimization ◽

10.1007/0-387-23539-6_1 ◽

2006 ◽

pp. 1-20

Keyword(s):

Optimization Problems ◽

Classical Optimization

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The Use of the Adjoint Method for Solving Typical Optimization Problems in Multibody Dynamics

Journal of Computational and Nonlinear Dynamics ◽

10.1115/1.4028417 ◽

2015 ◽

Vol 10 (6) ◽

Cited By ~ 17

Author(s):

Karin Nachbagauer ◽

Stefan Oberpeilsteiner ◽

Karim Sherif ◽

Wolfgang Steiner

Keyword(s):

Optimal Control ◽

Parameter Identification ◽

Multibody Dynamics ◽

Trajectory Tracking ◽

Adjoint Method ◽

Inverse Dynamics ◽

Optimization Problems ◽

Complicated Structure ◽

Wide Range ◽

Classical Optimization

The present paper illustrates the potential of the adjoint method for a wide range of optimization problems in multibody dynamics such as inverse dynamics and parameter identification. Although the equations and matrices included show a complicated structure, the additional effort when combining the standard forward solver to the adjoint backward solver is kept in limits. Therefore, the adjoint method shows an efficient way to incorporate inverse dynamics to engineering multibody applications, e.g., trajectory tracking or parameter identification in the field of robotics. The present paper studies examples for both, parameter identification and optimal control, and shows the potential of the adjoint method in solving classical optimization problems in multibody dynamics.

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