scholarly journals Multi-Agent Finite Impulse Response Optimizer for Numerical Optimization Problems

2020 ◽  
Author(s):  
Tasiransurini Ab Rahman ◽  
Nor Azlina Ab. Aziz ◽  
Zuwairie Ibrahim ◽  
Nor Hidayati Abdul Aziz ◽  
Mohd Ibrahim Shapiai ◽  
...  
Keyword(s):  
Impulse Response ◽  
Numerical Optimization ◽  
Optimization Problems ◽  
Finite Impulse Response ◽  
Population Based ◽  
Grey Wolf Optimizer ◽  
Metaheuristic Algorithm ◽  
Mathematical Functions ◽  
Random Mutation ◽  
Multi Agent

Abstract This paper investigates the potential of the ultimate iterative unbiased finite impulse response (UFIR) filter as a source of inspiration in a population-based metaheuristic algorithm. Here, a new algorithm inspired by the measurement and estimation procedures of the UFIR filter named the Multi-Agent Finite Impulse Response Optimizer (MAFIRO) for solving numerical optimization problems is proposed. MAFIRO works with a set of agents where each performs the measurement and estimation to find a solution. MAFIRO employs a random mutation of the best-so-far solution and the shrinking local neighborhood method to balance between the exploration and exploitation phases during the optimization process. Subsequently, the performance of MAFIRO is tested by solving the benchmark test suite of the IEEE Congress on Evolutionary Computation 2014. The benchmark is composed of 30 mathematical functions. The competency of MAFIRO is compared with the Particle Swarm Optimization algorithm, Genetic Algorithm, and Grey Wolf Optimizer. The results show that MAFIRO leads in 23 out of 30 functions and has the highest Friedman rank. MAFIRO performs significantly better than the other tested algorithms. Based on the findings, we show that the concept of the UFIR filter is a good inspiration for a population-based metaheuristic algorithm.

scholarly journals A Study on the Effect of Local Neighbourhood Parameter towards the Performance of SAFIRO

2018 ◽  
Vol 7 (4.27) ◽  
pp. 30 ◽  
Author(s):  
Tasiransurini Ab Rahman ◽  
Zuwairie Ibrahim ◽  
Nor Azlina Ab. Aziz ◽  
Nor Hidayati Abdul Aziz ◽  
Suad Khairi Mohammed ◽  
...  
Keyword(s):  
Impulse Response ◽  
Optimization Problems ◽  
Finite Impulse Response ◽  
Single Agent ◽  
Step Size ◽  
Random Mutation ◽  
Local Neighborhood ◽  
Post Hoc ◽  
Impulse Response Filter ◽  
Neighborhood Method

Single-agent Finite Impulse Response Optimizer (SAFIRO) is a recently proposed metaheuristic optimization algorithm which adopted the procedure of the ultimate unbiased finite impulse response filter (UFIR) in state estimation. In SAFIRO, a random mutation with shrinking local neighborhood method is employed during measurement phase to balance the exploration and the exploitation process. Beta, β, is one of the parameters used in the local neighborhood to control the step size. In this study, the effect of β towards the performance of SAFIRO is observed by assigning the value of 1, 5, 10, 15, and 20. The best setting of β for SAFIRO is also determined. The CEC2014 Benchmark Test Suite is used to evaluate the SAFIRO performance with different β values. Results show that the performance of β is depending on the problems to be optimized. 17 out of 30 functions show the best performance of SAFIRO by setting β = 10. Statistical analysis using Friedman test and Holm post hoc test were performed to rank the performance. β = 10 has the highest rank where its performance is significantly better than other values, but equivalent to β = 5 and β = 15. Hence, it is recommended to tune the β for best performance, however, β = 10 is a good value to be used in SAFIRO for solving optimization problems.  

scholarly journals Single-Agent Finite Impulse Response Optimizer for Numerical Optimization Problems

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 9358-9374 ◽  
Author(s):  
Tasiransurini Ab Rahman ◽  
Zuwairie Ibrahim ◽  
Nor Azlina Ab. Aziz ◽  
Shunyi Zhao ◽  
Nor Hidayati Abdul Aziz
Keyword(s):  
Impulse Response ◽  
Numerical Optimization ◽  
Optimization Problems ◽  
Finite Impulse Response ◽  
Single Agent

scholarly journals GMBO: Group Mean-Based Optimizer for Solving Various Optimization Problems

Mathematics ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 1190
Author(s):  
Mohammad Dehghani ◽  
Zeinab Montazeri ◽  
Štěpán Hubálovský
Keyword(s):  
Optimization Algorithm ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Random Search ◽  
Optimization Algorithms ◽  
Gravitational Search Algorithm ◽  
Main Idea ◽  
Population Based ◽  
Grey Wolf Optimizer ◽  
Whale Optimization

There are many optimization problems in the different disciplines of science that must be solved using the appropriate method. Population-based optimization algorithms are one of the most efficient ways to solve various optimization problems. Population-based optimization algorithms are able to provide appropriate solutions to optimization problems based on a random search of the problem-solving space without the need for gradient and derivative information. In this paper, a new optimization algorithm called the Group Mean-Based Optimizer (GMBO) is presented; it can be applied to solve optimization problems in various fields of science. The main idea in designing the GMBO is to use more effectively the information of different members of the algorithm population based on two selected groups, with the titles of the good group and the bad group. Two new composite members are obtained by averaging each of these groups, which are used to update the population members. The various stages of the GMBO are described and mathematically modeled with the aim of being used to solve optimization problems. The performance of the GMBO in providing a suitable quasi-optimal solution on a set of 23 standard objective functions of different types of unimodal, high-dimensional multimodal, and fixed-dimensional multimodal is evaluated. In addition, the optimization results obtained from the proposed GMBO were compared with eight other widely used optimization algorithms, including the Marine Predators Algorithm (MPA), the Tunicate Swarm Algorithm (TSA), the Whale Optimization Algorithm (WOA), the Grey Wolf Optimizer (GWO), Teaching–Learning-Based Optimization (TLBO), the Gravitational Search Algorithm (GSA), Particle Swarm Optimization (PSO), and the Genetic Algorithm (GA). The optimization results indicated the acceptable performance of the proposed GMBO, and, based on the analysis and comparison of the results, it was determined that the GMBO is superior and much more competitive than the other eight algorithms.

ONE IMPROVED AGENT GENETIC ALGORITHM — RING-LIKE AGENT GENETIC ALGORITHM FOR GLOBAL NUMERICAL OPTIMIZATION

2009 ◽  
Vol 26 (04) ◽  
pp. 479-502 ◽  
Author(s):  
BIN LIU ◽  
TEQI DUAN ◽  
YONGMING LI
Keyword(s):  
Genetic Algorithm ◽  
Numerical Optimization ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Genetic Operators ◽  
Global Numerical Optimization ◽  
Candidate Solution ◽  
Low Dimension ◽  
Multi Agent ◽  
Agent Structure

In this paper, a novel genetic algorithm — dynamic ring-like agent genetic algorithm (RAGA) is proposed for solving global numerical optimization problem. The RAGA combines the ring-like agent structure and dynamic neighboring genetic operators together to get better optimization capability. An agent in ring-like agent structure represents a candidate solution to the optimization problem. Any agent interacts with neighboring agents to evolve. With dynamic neighboring genetic operators, they compete and cooperate with their neighbors, and they can also use knowledge to increase energies. Global numerical optimization problems are the most important ones to verify the performance of evolutionary algorithm, especially of genetic algorithm and are mostly of interest to the corresponding researchers. In the corresponding experiments, several complex benchmark functions were used for optimization, several popular GAs were used for comparison. In order to better compare two agents GAs (MAGA: multi-agent genetic algorithm and RAGA), the several dimensional experiments (from low dimension to high dimension) were done. These experimental results show that RAGA not only is suitable for optimization problems, but also has more precise and more stable optimization results.

A new meta-heuristic optimization algorithm based on a paradigm from physics: string theory

2021 ◽  
Vol 41 (1) ◽  
pp. 1657-1675
Author(s):  
Luis Rodriguez ◽  
Oscar Castillo ◽  
Mario Garcia ◽  
Jose Soria
Keyword(s):  
String Theory ◽  
Optimization Algorithm ◽  
Fuzzy Inference ◽  
Fuzzy Controller ◽  
Optimization Problems ◽  
Flower Pollination Algorithm ◽  
Grey Wolf Optimizer ◽  
Mathematical Functions ◽  
Inference System ◽  
First Case

The main goal of this paper is to outline a new optimization algorithm based on String Theory, which is a relative new area of physics. The String Theory Algorithm (STA) is a nature-inspired meta-heuristic, which is based on studies about a theory stating that all the elemental particles that exist in the universe are strings, and the vibrations of these strings create all particles existing today. The newly proposed algorithm uses equations based on the laws of physics that are stated in String Theory. The main contribution in this proposed method is the new techniques that are devised in order to generate potential solutions in optimization problems, and we are presenting a detailed explanation and the equations involved in the new algorithm in order to solve optimization problems. In this case, we evaluate this new proposed meta-heuristic with three cases. The first case is of 13 traditional benchmark mathematical functions and a comparison with three different meta-heuristics is presented. The three algorithms are: Flower Pollination Algorithm (FPA), Firefly Algorithm (FA) and Grey Wolf Optimizer (GWO). The second case is the optimization of benchmark functions of the CEC 2015 Competition and we are also presenting a statistical comparison of these results with respect to FA and GWO. In addition, we are presenting a third case, which is the optimization of a fuzzy inference system (FIS), specifically finding the optimal design of a fuzzy controller, where the main goal is to optimize the membership functions of the FIS. It is important to mention that we used these study cases in order to analyze the proposed meta-heuristic with: basic problems, complex problems and control problems. Finally, we present the performance, results and conclusions of the new proposed meta-heuristic.

An Improved Differential Evolution Algorithm for Numerical Optimization Problems

2013 ◽  
Vol 415 ◽  
pp. 349-352
Author(s):  
Hong Wei Zhao ◽  
Hong Gang Xia
Keyword(s):  
Differential Evolution ◽  
Numerical Optimization ◽  
Optimization Problems ◽  
Differential Evolution Algorithm ◽  
Population Based ◽  
Local Optimum ◽  
Local Optima ◽  
De Algorithm ◽  
Evolution Algorithm ◽  
Improved Differential Evolution Algorithm

Differential evolution (DE) is a population-based stochastic function minimizer (or maximizer), whose simple yet powerful and straightforward features make it very attractive for numerical optimization. However, DE is easy to trapped into local optima. In this paper, an improved differential evolution algorithm (IDE) proposed to speed the convergence rate of DE and enhance the global search of DE. The IDE employed a new mutation operation and modified crossover operation. The former can rapidly enhance the convergence of the MDE, and the latter can prevent the MDE from being trapped into the local optimum effectively. Besides, we dynamic adjust the scaling factor (F) and the crossover rate (CR), which is aimed at further improving algorithm performance. Based on several benchmark experiment simulations, the IDE has demonstrated stronger convergence and stability than original differential (DE) algorithm and other algorithms (PSO and JADE) that reported in recent literature.

scholarly journals An Improved Harmony Search Based on Teaching-Learning Strategy for Unconstrained Optimization Problems

2013 ◽  
Vol 2013 ◽  
pp. 1-29 ◽  
Author(s):  
Shouheng Tuo ◽  
Longquan Yong ◽  
Tao Zhou
Keyword(s):  
High Dimension ◽  
Learning Strategy ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Harmony Search ◽  
Population Based ◽  
Population Diversity ◽  
Random Mutation ◽  
Complex Optimization ◽  
Teaching Learning

Harmony search (HS) algorithm is an emerging population-based metaheuristic algorithm, which is inspired by the music improvisation process. The HS method has been developed rapidly and applied widely during the past decade. In this paper, an improved global harmony search algorithm, named harmony search based on teaching-learning (HSTL), is presented for high dimension complex optimization problems. In HSTL algorithm, four strategies (harmony memory consideration, teaching-learning strategy, local pitch adjusting, and random mutation) are employed to maintain the proper balance between convergence and population diversity, and dynamic strategy is adopted to change the parameters. The proposed HSTL algorithm is investigated and compared with three other state-of-the-art HS optimization algorithms. Furthermore, to demonstrate the robustness and convergence, the success rate and convergence analysis is also studied. The experimental results of 31 complex benchmark functions demonstrate that the HSTL method has strong convergence and robustness and has better balance capacity of space exploration and local exploitation on high dimension complex optimization problems.

scholarly journals On the Binarization of Grey Wolf Optimizer‎: ‎A Novel Binary Optimizer Algorithm

2021 ◽  
Author(s):  
Mehdy Roayaei
Keyword(s):  
Combinatorial Optimization ◽  
Real World ◽  
Optimization Problems ◽  
Population Based ◽  
Grey Wolf Optimizer ◽  
Grey Wolf ◽  
Combinatorial Optimization Problems ◽  
Hunting Behaviour ◽  
Set Operations

Abstract ‎Grey Wolf Optimizer (GWO) is a population-based evolutionary algorithm inspired by the hunting behaviour of grey wolves‎. ‎GWO‎, ‎in its basic form‎, ‎is a real coded algorithm‎, ‎therefore‎, ‎it needs modifications to deal with binary optimization problems‎. ‎In this paper‎, ‎we review previous works on binarization of GWO‎, ‎and classify them with respect to their encoding scheme‎, ‎updating strategy‎, ‎and transfer function‎. ‎Then‎, ‎we propose a novel binary GWO algorithm (named SetGWO)‎, ‎which is based on set encoding and uses set operations in its updating strategy‎. ‎Experimental results on different real-world combinatorial optimization problems and different datasets‎, ‎show that SetGWO outperforms other existing binary GWO algorithms in terms of quality of solutions‎, ‎running time‎, ‎and scalability‎.

scholarly journals Heterogeneous Differential Evolution for Numerical Optimization

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Hui Wang ◽  
Wenjun Wang ◽  
Zhihua Cui ◽  
Hui Sun ◽  
Shahryar Rahnamayan
Keyword(s):  
Differential Evolution ◽  
Numerical Optimization ◽  
Large Scale ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Population Based ◽  
Stochastic Search ◽  
Test Problems ◽  
Exploration And Exploitation ◽  
Large Scale Problems

Differential evolution (DE) is a population-based stochastic search algorithm which has shown a good performance in solving many benchmarks and real-world optimization problems. Individuals in the standard DE, and most of its modifications, exhibit the same search characteristics because of the use of the same DE scheme. This paper proposes a simple and effective heterogeneous DE (HDE) to balance exploration and exploitation. In HDE, individuals are allowed to follow different search behaviors randomly selected from a DE scheme pool. Experiments are conducted on a comprehensive set of benchmark functions, including classical problems and shifted large-scale problems. The results show that heterogeneous DE achieves promising performance on a majority of the test problems.

scholarly journals A Comparison of Evolutionary Computation Techniques for IIR Model Identification

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Erik Cuevas ◽  
Jorge Gálvez ◽  
Salvador Hinojosa ◽  
Omar Avalos ◽  
Daniel Zaldívar ◽  
...  
Keyword(s):  
Evolutionary Computation ◽  
Impulse Response ◽  
Optimization Problems ◽  
Finite Impulse Response ◽  
Model Identification ◽  
Optimization Techniques ◽  
Infinite Impulse Response ◽  
Computation Techniques ◽  
Recent Developments ◽  
Complex Optimization

System identification is a complex optimization problem which has recently attracted the attention in the field of science and engineering. In particular, the use of infinite impulse response (IIR) models for identification is preferred over their equivalent FIR (finite impulse response) models since the former yield more accurate models of physical plants for real world applications. However, IIR structures tend to produce multimodal error surfaces whose cost functions are significantly difficult to minimize. Evolutionary computation techniques (ECT) are used to estimate the solution to complex optimization problems. They are often designed to meet the requirements of particular problems because no single optimization algorithm can solve all problems competitively. Therefore, when new algorithms are proposed, their relative efficacies must be appropriately evaluated. Several comparisons among ECT have been reported in the literature. Nevertheless, they suffer from one limitation: their conclusions are based on the performance of popular evolutionary approaches over a set of synthetic functions with exact solutions and well-known behaviors, without considering the application context or including recent developments. This study presents the comparison of various evolutionary computation optimization techniques applied to IIR model identification. Results over several models are presented and statistically validated.

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