scholarly journals An Enhanced Slime Mould Algorithm and Its Application for Digital IIR Filter Design

2021 ◽  
Vol 2021 ◽  
pp. 1-23
Author(s):  
Xiaodan Liang ◽  
Dong Wu ◽  
Yang Liu ◽  
Maowei He ◽  
Liling Sun
Keyword(s):  
Optimization Problems ◽  
Filter Design ◽  
Optimal Solution ◽  
Population Diversity ◽  
Convergence Speed ◽  
Superior Performance ◽  
Movement Behavior ◽  
Initial Population ◽  
Slime Mould ◽  
Iir Filter

In the past few decades, metaheuristic algorithms (MA) have been developed tremendously and have been successfully applied in many fields. In recent years, a large number of new MA have been proposed. Slime mould algorithm (SMA) is a novel swarm-based intelligence optimization algorithm. SMA solves the optimization problem by imitating the foraging and movement behavior of slime mould. It can effectively obtain a promising global optimal solution. However, it still suffers some shortcomings such as the unstable convergence speed, the imprecise search accuracy, and incapability of identifying a local optimal solution when faced with complicated optimization problems. With the purpose of overcoming the shortcomings of SMA, this paper proposed a multistrategy enhanced version of SMA called ESMA. The three enhanced strategies are chaotic initialization strategy (CIS), orthogonal learning strategy (OLS), and boundary reset strategy (BRS). The CIS is used to generate an initial population with diversity in the early stage of ESMA, which can increase the convergence speed of the algorithm and the quality of the final solution. Then, the OLS is used to discover the useful information of the best solutions and offer a potential search direction, which enhances the local search ability and raises the convergence rate. Finally, the BRS is used to correct individual positions, which ensures the population diversity and enhances the overall search capabilities of ESMA. The performance of ESMA was validated on the 30 IEEE CEC2014 functions and three IIR model identification problems, compared with other nine well-regarded and state-of-the-art algorithms. Simulation results and analysis prove that the ESMA has a superior performance. The three strategies involved in ESMA have significantly improved the performance of the basic SMA.

scholarly journals Intelligent discrete particle swarm optimization for multiprocessor task scheduling problem

2016 ◽  
Vol 11 (1) ◽  
pp. 58-67 ◽  
Author(s):  
S Sarathambekai ◽  
K Umamaheswari
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Initial Population ◽  
Discrete Particle Swarm Optimization ◽  
Swarm Optimization ◽  
Discrete Particle

Discrete particle swarm optimization is one of the most recently developed population-based meta-heuristic optimization algorithm in swarm intelligence that can be used in any discrete optimization problems. This article presents a discrete particle swarm optimization algorithm to efficiently schedule the tasks in the heterogeneous multiprocessor systems. All the optimization algorithms share a common algorithmic step, namely population initialization. It plays a significant role because it can affect the convergence speed and also the quality of the final solution. The random initialization is the most commonly used method in majority of the evolutionary algorithms to generate solutions in the initial population. The initial good quality solutions can facilitate the algorithm to locate the optimal solution or else it may prevent the algorithm from finding the optimal solution. Intelligence should be incorporated to generate the initial population in order to avoid the premature convergence. This article presents a discrete particle swarm optimization algorithm, which incorporates opposition-based technique to generate initial population and greedy algorithm to balance the load of the processors. Make span, flow time, and reliability cost are three different measures used to evaluate the efficiency of the proposed discrete particle swarm optimization algorithm for scheduling independent tasks in distributed systems. Computational simulations are done based on a set of benchmark instances to assess the performance of the proposed algorithm.

scholarly journals A Tent Marine Predators Algorithm with Estimation Distribution Algorithm and Gaussian Random Walk for Continuous Optimization Problems

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Chang-Jian Sun ◽  
Fang Gao
Keyword(s):  
Random Walk ◽  
Population Distribution ◽  
Optimization Problems ◽  
Continuous Optimization ◽  
Population Diversity ◽  
Initial Population ◽  
Local Optimum ◽  
Marine Predators ◽  
Gaussian Random Walk ◽  
Estimation Distribution Algorithm

The marine predators algorithm (MPA) is a novel population-based optimization method that has been widely used in real-world optimization applications. However, MPA can easily fall into a local optimum because of the lack of population diversity in the late stage of optimization. To overcome this shortcoming, this paper proposes an MPA variant with a hybrid estimation distribution algorithm (EDA) and a Gaussian random walk strategy, namely, HEGMPA. The initial population is constructed using cubic mapping to enhance the diversity of individuals in the population. Then, EDA is adapted into MPA to modify the evolutionary direction using the population distribution information, thus improving the convergence performance of the algorithm. In addition, a Gaussian random walk strategy with medium solution is used to help the algorithm get rid of stagnation. The proposed algorithm is verified by simulation using the CEC2014 test suite. Simulation results show that the performance of HEGMPA is more competitive than other comparative algorithms, with significant improvements in terms of convergence accuracy and convergence speed.

Dynamic Optimization of Chemical Processes Based on Modified Sailfish Optimizer Combined with an Equal Division Method

Processes ◽  
2021 ◽  
Vol 9 (10) ◽  
pp. 1806
Author(s):  
Yuedong Zhang ◽  
Yuanbin Mo
Keyword(s):  
Dynamic Optimization ◽  
Production Efficiency ◽  
Chemical Engineering ◽  
Optimization Problems ◽  
Optimal Solution ◽  
Population Diversity ◽  
Equal Division ◽  
Dynamic Optimization Problems ◽  
Increase Production Efficiency ◽  
Chaotic Mapping

The optimal solution of the chemical dynamic optimization problem is the basis of automatic control operation in the chemical process, which can reduce energy consumption, increase production efficiency, and maximize economic benefit. In this paper, a modified sailfish optimizer (MSFO) combined with an equal division method is proposed for solving chemical dynamic optimization problems. Based on the basic sailfish optimizer, firstly, the tent chaotic mapping strategy is introduced to disturb the initialization of sailfish and sardine populations to avoid the loss of population diversity. Secondly, an adaptive linear reduction strategy of attack parameters is proposed to enhance the exploration and exploitation ability of sailfish. Thirdly, the updating formula of sardine position is modified, and the global optimal solution is used to attract all sardine positions, which can avoid the premature phenomenon of the algorithm. Eventually, the MSFO is applied to solve six classical optimization cases of chemical engineering to evaluate its feasibility. The experimental results are analyzed and compared with other optimization methods to prove the superiority of the MSFO in solving chemical dynamic optimization problems.

An evolutionary based framework for many-objective optimization problems

2018 ◽  
Vol 35 (4) ◽  
pp. 1805-1828 ◽  
Author(s):  
Kimia Bazargan Lari ◽  
Ali Hamzeh
Keyword(s):  
Optimization Problems ◽  
State Of The Art ◽  
Fitness Function ◽  
Population Diversity ◽  
Numerical Results ◽  
Superior Performance ◽  
High Dimensional ◽  
Test Problems ◽  
Content Type ◽  
Objective Space

Purpose Recently, many-objective optimization evolutionary algorithms have been the main issue for researchers in the multi-objective optimization community. To deal with many-objective problems (typically for four or more objectives) some modern frameworks are proposed which have the potential of achieving the finest non-dominated solutions in many-objective spaces. The effectiveness of these algorithms deteriorates greatly as the problem’s dimension increases. Diversity reduction in the objective space is the main reason of this phenomenon. Design/methodology/approach To properly deal with this undesirable situation, this work introduces an indicator-based evolutionary framework that can preserve the population diversity by producing a set of discriminated solutions in high-dimensional objective space. This work attempts to diversify the objective space by proposing a fitness function capable of discriminating the chromosomes in high-dimensional space. The numerical results prove the potential of the proposed method, which had superior performance in most of test problems in comparison with state-of-the-art algorithms. Findings The achieved numerical results empirically prove the superiority of the proposed method to state-of-the-art counterparts in the most test problems of a known artificial benchmark. Originality/value This paper provides a new interpretation and important insights into the many-objective optimization realm by emphasizing on preserving the population diversity.

Researches on Function Optimization Problems Based on Chaotic Immune Genetic Algorithms

2012 ◽  
Vol 616-618 ◽  
pp. 2064-2067
Author(s):  
Yong Gang Che ◽  
Chun Yu Xiao ◽  
Chao Hai Kang ◽  
Ying Ying Li ◽  
Li Ying Gong
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Optimization Problems ◽  
Chaotic Map ◽  
Solution Space ◽  
Population Diversity ◽  
Function Optimization ◽  
Initial Population ◽  
Immune Genetic Algorithm ◽  
Complex Functions

To solve the primary problems in genetic algorithms, such as slow convergence speed, poor local searching capability and easy prematurity, the immune mechanism is introduced into the genetic algorithm, and thus population diversity is maintained better, and the phenomena of premature convergence and oscillation are reduced. In order to compensate the defects of immune genetic algorithm, the Hénon chaotic map, which is introduced on the above basis, makes the generated initial population uniformly distributed in the solution space, eventually, the defect of data redundancy is reduced and the quality of evolution is improved. The proposed chaotic immune genetic algorithm is used to optimize the complex functions, and there is an analysis compared with the genetic algorithm and the immune genetic algorithm, the feasibility and effectiveness of the proposed algorithm are proved from the perspective of simulation experiments.

scholarly journals Surrogate Modeling Approaches for Multiobjective Optimization: Methods, Taxonomy, and Results

2020 ◽  
Vol 26 (1) ◽  
pp. 5
Author(s):  
Kalyanmoy Deb ◽  
Proteek Chandan Roy ◽  
Rayan Hussein
Keyword(s):  
Multiobjective Optimization ◽  
Optimization Problems ◽  
Optimal Solution ◽  
Adaptive Strategy ◽  
Optimization Methods ◽  
Superior Performance ◽  
Pareto Optimal ◽  
Optimization Strategy ◽  
Conflicting Objectives ◽  
The Individual

Most practical optimization problems are comprised of multiple conflicting objectives and constraints which involve time-consuming simulations. Construction of metamodels of objectives and constraints from a few high-fidelity solutions and a subsequent optimization of metamodels to find in-fill solutions in an iterative manner remain a common metamodeling based optimization strategy. The authors have previously proposed a taxonomy of 10 different metamodeling frameworks for multiobjective optimization problems, each of which constructs metamodels of objectives and constraints independently or in an aggregated manner. Of the 10 frameworks, five follow a generative approach in which a single Pareto-optimal solution is found at a time and other five frameworks were proposed to find multiple Pareto-optimal solutions simultaneously. Of the 10 frameworks, two frameworks (M3-2 and M4-2) are detailed here for the first time involving multimodal optimization methods. In this paper, we also propose an adaptive switching based metamodeling (ASM) approach by switching among all 10 frameworks in successive epochs using a statistical comparison of metamodeling accuracy of all 10 frameworks. On 18 problems from three to five objectives, the ASM approach performs better than the individual frameworks alone. Finally, the ASM approach is compared with three other recently proposed multiobjective metamodeling methods and superior performance of the ASM approach is observed. With growing interest in metamodeling approaches for multiobjective optimization, this paper evaluates existing strategies and proposes a viable adaptive strategy by portraying importance of using an ensemble of metamodeling frameworks for a more reliable multiobjective optimization for a limited budget of solution evaluations.

scholarly journals Design of Finite Impulse Response Filters Based on Genetic Algorithm

2013 ◽  
Vol 6 (3) ◽  
pp. 28-39
Author(s):  
Raaed Faleh Hassan ◽  
Ali Subhi Abbood
Keyword(s):  
Genetic Algorithm ◽  
Impulse Response ◽  
Optimization Problems ◽  
Filter Design ◽  
Finite Impulse Response ◽  
Optimal Solution ◽  
Fir Filter ◽  
Language Version ◽  
Finite Impulse Response Filters ◽  
Matlab Programming

Genetic Algorithms (GAs) are used to solve many optimization problems in science and engineering such as pattern recognition, robotics, biology, medicine, and many other applications. The aim of this paper is to describe a method of designing Finite Impulse Response (FIR) filter using Genetic Algorithm (GA). In this paper, the Genetic Algorithm not only used for searching the optimal coefficients, but also it is used to find the minimum number of Taps, and hence minimize the number of multipliers and adders that can be used in the design of the FIR filter. The Evolutionary Programming is the best search procedure and most powerful than Linear Programming in providing the optimal solution that is desired to minimize the ripple content in both passband and stopband. The algorithm generates a population of genomes that represents the filter coefficient and the number of taps, where new genomes are generated by crossover and mutation operations methods. Our proposed genetic technique has able to give better result compare to other method.The FIR filter design using Genetic Algorithm is simulated using MATLAB programming language version 7.6.0.324 (R2008a).

scholarly journals An Improved Particle Swarm Optimization with Gaussian Disturbance

2018 ◽  
Vol 232 ◽  
pp. 03015
Author(s):  
Changjun Wen ◽  
Changlian Liu ◽  
Heng Zhang ◽  
Hongliang Wang
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Convergence Speed ◽  
Local Optimum ◽  
Swarm Optimization ◽  
Local Optimal Solution ◽  
Gaussian Disturbance ◽  
Griewank Function

The particle swarm optimization (PSO) is a widely used tool for solving optimization problems in the field of engineering technology. However, PSO is likely to fall into local optimum, which has the disadvantages of slow convergence speed and low convergence precision. In view of the above shortcomings, a particle swarm optimization with Gaussian disturbance is proposed. With introducing the Gaussian disturbance in the self-cognition part and social cognition part of the algorithm, this method can improve the convergence speed and precision of the algorithm, which can also improve the ability of the algorithm to escape the local optimal solution. The algorithm is simulated by Griewank function after the several evolutionary modes of GDPSO algorithm are analyzed. The experimental results show that the convergence speed and the optimization precision of the GDPSO is better than that of PSO.

scholarly journals Masi Entropy for Satellite Color Image Segmentation Using Tournament-Based Lévy Multiverse Optimization Algorithm

2019 ◽  
Vol 11 (8) ◽  
pp. 942 ◽  
Author(s):  
Jia ◽  
Peng ◽  
Song ◽  
Oliva ◽  
Lang ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Optimization Algorithm ◽  
Optimization Problems ◽  
Color Image ◽  
Fitness Function ◽  
Similarity Index ◽  
Optimal Solution ◽  
Population Diversity ◽  
Color Image Segmentation ◽  
Masi Entropy

A novel multilevel threshold segmentation method for color satellite images based on Masi entropy is proposed in this paper. Lévy multiverse optimization algorithm (LMVO) has a strong advantage over the traditional multiverse optimization algorithm (MVO) in finding the optimal solution for the segmentation in the three channels of an RGB image. As the work advancement introduces a Lévy multiverse optimization algorithm which uses tournament selection instead of roulette wheel selection, and updates some formulas in the algorithm with mutation factor. Then, the proposal is called TLMVO, and another advantage is that the population diversity of the algorithm in the latest iterations is maintained. The Masi entropy is used as an application and combined with the improved TLMVO algorithm for satellite color image segmentation. Masi entropy combines the additivity of Renyi entropy and the non-extensibility of Tsallis entropy. By increasing the number of thesholds, the quality of segmenttion becomes better, then the dimensionality of the problem also increases. Fitness function value, average CPU running time, Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM) and Feature Similarity Index (FSIM) were used to evaluate the segmentation results. Further statistical evaluation was given by Wilcoxon's rank sum test and Friedman test. The experimental results show that the TLMVO algorithm has wide adaptability to high-dimensional optimization problems, and has obvious advantages in objective function value, image quality detection, convergence performance and robustness.

scholarly journals A Multi-Strategy Marine Predator Algorithm and Its Application in Joint Regularization Semi-Supervised ELM

Mathematics ◽  
2021 ◽  
Vol 9 (3) ◽  
pp. 291
Author(s):  
Wenbiao Yang ◽  
Kewen Xia ◽  
Tiejun Li ◽  
Min Xie ◽  
Fei Song
Keyword(s):  
Extreme Learning Machine ◽  
Learning Strategy ◽  
Classification Performance ◽  
Population Diversity ◽  
Convergence Speed ◽  
Control Factor ◽  
Initial Population ◽  
Local Optimum ◽  
Marine Predator ◽  
Learning Machine

A novel semi-supervised learning method is proposed to better utilize labeled and unlabeled samples to improve classification performance. However, there is exists the limitation that Laplace regularization in a semi-supervised extreme learning machine (SSELM) tends to lead to poor generalization ability and it ignores the role of labeled information. To solve the above problems, a Joint Regularized Semi-Supervised Extreme Learning Machine (JRSSELM) is proposed, which uses Hessian regularization instead of Laplace regularization and adds supervised information regularization. In order to solve the problem of slow convergence speed and the easy to fall into local optimum of marine predator algorithm (MPA), a multi-strategy marine predator algorithm (MSMPA) is proposed, which first uses a chaotic opposition learning strategy to generate high-quality initial population, then uses adaptive inertia weights and adaptive step control factor to improve the exploration, utilization, and convergence speed, and then uses neighborhood dimensional learning strategy to maintain population diversity. The parameters in JRSSELM are then optimized using MSMPA. The MSMPA-JRSSELM is applied to logging oil formation identification. The experimental results show that MSMPA shows obvious superiority and strong competitiveness in terms of convergence accuracy and convergence speed. Also, the classification performance of MSMPA-JRSSELM is better than other classification methods, and the practical application is remarkable.

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