scholarly journals Chaotic Jaya Approaches to Solving Electromagnetic Optimization Benchmark Problems

Telecom ◽  
2021 ◽  
Vol 2 (2) ◽  
pp. 222-231
Author(s):  
Leandro dos S. Coelho ◽  
Viviana C. Mariani ◽  
Sotirios K. Goudos ◽  
Achilles D. Boursianis ◽  
Konstantinos Kokkinidis ◽  
...  
Keyword(s):  
Optimization Problems ◽  
Population Based ◽  
Benchmark Problems ◽  
Jaya Algorithm ◽  
Solution Quality ◽  
Chaotic Mapping ◽  
Logistic Mapping ◽  
Essential Idea ◽  
Convergence Performance ◽  
Application Fields

The Jaya optimization algorithm is a simple, fast, robust, and powerful population-based stochastic metaheuristic that in recent years has been successfully applied in a variety of global optimization problems in various application fields. The essential idea of the Jaya algorithm is that the searching agents try to change their positions toward the best obtained solution by avoiding the worst solution at every generation. The important difference between Jaya and other metaheuristics is that Jaya does not require the tuning of its control, except for the maximum number of iterations and population size parameters. However, like other metaheuristics, Jaya still has the dilemma of an appropriate tradeoff between its exploration and exploitation abilities during the evolution process. To enhance the convergence performance of the standard Jaya algorithm in the continuous domain, chaotic Jaya (CJ) frameworks based on chaotic sequences are proposed in this paper. In order to obtain the performance of the standard Jaya and CJ approaches, tests related to electromagnetic optimization using two different benchmark problems are conducted. These are the Loney’s solenoid benchmark and a brushless direct current (DC) motor benchmark. Both problems are realized to evaluate the effectiveness and convergence rate. The simulation results and comparisons with the standard Jaya algorithm demonstrated that the performance of the CJ approaches based on Chebyshev-type chaotic mapping and logistic mapping can be competitive results in terms of both efficiency and solution quality in electromagnetics optimization.

Self-adaptively Commensal Learning-based Jaya Algorithm with Multi-populations and its Application

2021 ◽  
Author(s):  
Zuanjia Xie ◽  
Chunliang Zhang ◽  
Haibin Ouyang ◽  
Steven Li ◽  
Liqun Gao
Keyword(s):  
Learning Strategy ◽  
Optimization Problems ◽  
State Of The Art ◽  
Global Optimum ◽  
Benchmark Problems ◽  
Series System ◽  
Jaya Algorithm ◽  
Roulette Wheel Selection ◽  
Roulette Wheel ◽  
Distribution Scheme

Abstract Jaya algorithm is an advanced optimization algorithm, which has been applied to many real-world optimization problems. Jaya algorithm has better performance in some optimization field. However, Jaya algorithm exploration capability is not better. In order to enhance exploration capability of the Jaya algorithm, a self-adaptively commensal learning-based Jaya algorithm with multi-populations (Jaya-SCLMP) is presented in this paper. In Jaya-SCLMP, a commensal learning strategy is used to increase the probability of finding the global optimum, in which the person history best and worst information is used to explore new solution area. Moreover, a multi-populations strategy based on Gaussian distribution scheme and learning dictionary is utilized to enhance the exploration capability, meanwhile every sub-population employed three Gaussian distributions at each generation, roulette wheel selection is employed to choose a scheme based on learning dictionary. The performance of Jaya-SCLMP is evaluated based on 28 CEC 2013 unconstrained benchmark problems. In addition, three reliability problems, i.e. complex (bridge) system, series system and series-parallel system are selected. Compared with several Jaya variants and several state-of-the-art other algorithms, the experimental results reveal that Jaya-SCLMP is effective.

Constrained Economic Optimization of Shell-and-Tube Heat Exchangers Using a Self-Adaptive Multipopulation Elitist-Jaya Algorithm

2018 ◽  
Vol 10 (4) ◽  
Author(s):  
R. Venkata Rao ◽  
Ankit Saroj
Keyword(s):  
Optimization Problems ◽  
Search Algorithm ◽  
Harmony Search ◽  
Benchmark Problems ◽  
Jaya Algorithm ◽  
Economic Optimization ◽  
Setup Cost ◽  
Tube Heat Exchanger ◽  
Shell And Tube ◽  
Self Adaptive

This paper explores the use of a self-adaptive multipopulation elitist (SAMPE) Jaya algorithm for the economic optimization of shell-and-tube heat exchanger (STHE) design. Three different optimization problems of STHE are considered in this work. The same problems were earlier attempted by other researchers using genetic algorithm (GA), particle swarm optimization (PSO) algorithm, biogeography-based optimization (BBO), imperialist competitive algorithm (ICA), artificial bee colony (ABC), cuckoo-search algorithm (CSA), intelligence-tuned harmony search (ITHS), and cohort intelligence (CI) algorithm. The Jaya algorithm is a newly developed algorithm and it does not have any algorithmic-specific parameters to be tuned except the common control parameters of number of iterations and population size. The search mechanism of the Jaya algorithm is upgraded in this paper by using the multipopulation search scheme with the elitism. The SAMPE-Jaya algorithm is proposed in this paper to optimize the setup cost and operational cost of STHEs simultaneously. The performance of the proposed SAPME-Jaya algorithm is tested on four well-known constrained, ten unconstrained standard benchmark problems, and three STHE design optimization problems. The results of computational experiments proved the superiority of the proposed method over the latest reported methods used for the optimization of the same problems.

scholarly journals Tree-Seed Algorithm for Large-Scale Binary Optimization

2018 ◽  
Vol 3 (1) ◽  
pp. 48 ◽  
Author(s):  
Ahmet Cevahir Cinar ◽  
Hazim Iscan ◽  
Mustafa Servet Kiran
Keyword(s):  
Large Scale ◽  
Optimization Problems ◽  
Logic Gate ◽  
Continuous Optimization ◽  
Population Based ◽  
Benchmark Problems ◽  
Binary Optimization ◽  
Continuous Optimization Problems ◽  
Computation Algorithms ◽  
Tree Seed

Population-based swarm or evolutionary computation algorithms in optimization are attracted the interest of the researchers due their simple structure, optimization performance, easy-adaptation. Binary optimization problems can be also solved by using these algorithms. This paper focuses on solving large scale binary optimization problems by using Tree-Seed Algorithm (TSA) proposed for solving continuous optimization problems by imitating relationship between the trees and their seeds in nature. The basic TSA is modified by using xor logic gate for solving binary optimization problems in this study. In order to investigate the performance of the proposed algorithm, the numeric benchmark problems with the different dimensions are considered and obtained results show that the proposed algorithm produces effective and comparable solutions in terms of solution quality.Keywords: binary optimization, tree-seed algorithm, xor-gate, large-scale optimization

Population Based Equilibrium in Hybrid SA/PSO for Combinatorial Optimization

2020 ◽  
Vol 12 (2) ◽  
pp. 74-86 ◽  
Author(s):  
Kenneth Brezinski ◽  
Michael Guevarra ◽  
Ken Ferens
Keyword(s):  
Combinatorial Optimization ◽  
Optimization Problems ◽  
Parameter Tuning ◽  
Population Based ◽  
Convergence Time ◽  
Benchmark Problems ◽  
Swarm Optimization ◽  
Combinatorial Optimization Problems ◽  
Benchmark Instances

This article introduces a hybrid algorithm combining simulated annealing (SA) and particle swarm optimization (PSO) to improve the convergence time of a series of combinatorial optimization problems. The implementation carried out a dynamic determination of the equilibrium loops in SA through a simple, yet effective determination based on the recent performance of the swarm members. In particular, the authors demonstrated that strong improvements in convergence time followed from a marginal decrease in global search efficiency compared to that of SA alone, for several benchmark instances of the traveling salesperson problem (TSP). Following testing on 4 additional city list TSP problems, a 30% decrease in convergence time was achieved. All in all, the hybrid implementation minimized the reliance on parameter tuning of SA, leading to significant improvements to convergence time compared to those obtained with SA alone for the 15 benchmark problems tested.

scholarly journals Lion Optimization Algorithm (LOA): A nature-inspired metaheuristic algorithm

2015 ◽  
Vol 3 (1) ◽  
pp. 24-36 ◽  
Author(s):  
Maziar Yazdani ◽  
Fariborz Jolai
Keyword(s):  
Optimization Algorithm ◽  
High Performance ◽  
Optimization Problems ◽  
Population Based ◽  
Metaheuristic Algorithms ◽  
Benchmark Problems ◽  
The Past ◽  
Complex Optimization ◽  
Lion Optimization Algorithm ◽  
Population Based Algorithm

Abstract During the past decade, solving complex optimization problems with metaheuristic algorithms has received considerable attention among practitioners and researchers. Hence, many metaheuristic algorithms have been developed over the last years. Many of these algorithms are inspired by various phenomena of nature. In this paper, a new population based algorithm, the Lion Optimization Algorithm (LOA), is introduced. Special lifestyle of lions and their cooperation characteristics has been the basic motivation for development of this optimization algorithm. Some benchmark problems are selected from the literature, and the solution of the proposed algorithm has been compared with those of some well-known and newest meta-heuristics for these problems. The obtained results confirm the high performance of the proposed algorithm in comparison to the other algorithms used in this paper.

scholarly journals Improved Method for Parallelization of Evolutionary Metaheuristics

Mathematics ◽  
2020 ◽  
Vol 8 (9) ◽  
pp. 1476
Author(s):  
Diego Díaz ◽  
Pablo Valledor ◽  
Borja Ena ◽  
Miguel Iglesias ◽  
César Menéndez
Keyword(s):  
Propagation Rate ◽  
Population Based ◽  
New Method ◽  
Benchmark Problems ◽  
Main Concept ◽  
Traveling Salesman Problems ◽  
Solution Quality ◽  
Complex Design ◽  
Naive Approach

This paper introduces a method for the distribution of any and all population-based metaheuristics. It improves on the naive approach, independent multiple runs, while adding negligible overhead. Existing methods that coordinate instances across a cluster typically require some compromise of more complex design, higher communication loads, and solution propagation rate, requiring more work to develop and more resources to run. The aim of the new method is not to achieve state-of-the-art results, but rather to provide a better baseline method than multiple independent runs. The main concept of the method is that one of the instances receives updates with the current best solution of all other instances. This work describes the general approach and its particularization to both genetic algorithms and ant colony optimization for solving Traveling Salesman Problems (TSPs). It also includes extensive tests on the TSPLIB benchmark problems of resulting quality of the solutions and anytime performance (solution quality versus time to reach it). These tests show that the new method yields better solutions for about two thirds of the problems and equivalent solutions in the remaining third, and consistently exhibits better anytime performance.

Improvements of bat algorithm using crossover technique and hybridization with Nelder-Mead simplex method

2019 ◽  
Vol 38 (3) ◽  
pp. 977-989
Author(s):  
Bourahla Kheireddine ◽  
Belli Zoubida ◽  
Hacib Tarik
Keyword(s):  
Finite Element ◽  
Simplex Method ◽  
Design Methodology ◽  
Optimization Problems ◽  
Bat Algorithm ◽  
Benchmark Problems ◽  
Solution Quality ◽  
Content Type ◽  
Genetic Algorithm Method ◽  
Element Computation

Purpose This study aims to improve the bat algorithm (BA) performance for solving optimization problems in electrical engineering. Design/methodology/approach For this task, two strategies were investigated. The first one is based on including the crossover technique into classical BA, in the same manner as in the genetic algorithm method. Therefore, the newly generated version of BA is called the crossover–bat algorithm (C-BA). In the second strategy, a hybridization of the BA with the Nelder–Mead (NM) simplex method was performed; it gives the NM-BA algorithm. Findings First, the proposed strategies were applied to solve a set of two standard benchmark problems; then, they were applied to solve the TEAM workshop problem 25, where an electromagnetic field was computed by use of the 2D non-linear finite element method. Both optimization algorithms and finite element computation tool were implemented under MATLAB. Originality/value The two proposed optimization strategies, C-BA and NM-BA, have allowed good improvements of classical BA, generally known for its poor solution quality and slow convergence rate.

A self-adaptive bat algorithm for the truck and trailer routing problem

2018 ◽  
Vol 35 (1) ◽  
pp. 108-135 ◽  
Author(s):  
Chao Wang ◽  
Shengchuan Zhou ◽  
Yang Gao ◽  
Chao Liu
Keyword(s):  
Optimization Problems ◽  
Bat Algorithm ◽  
Computational Time ◽  
Benchmark Problems ◽  
Solution Quality ◽  
Heuristic Solution ◽  
Routing Problem ◽  
Content Type ◽  
Combinatorial Optimization Problems ◽  
Self Adaptive

Purpose The purpose of this paper is to provide an effective solution method for the truck and trailer routing problem (TTRP) which is one of the important NP-hard combinatorial optimization problems owing to its multiple real-world applications. It is a generalization of the famous vehicle routing problem (VRP), involving a group of geographically scattered customers served by the vehicle fleet including trucks and trailers. Design/methodology/approach The meta-heuristic solution approach based on bat algorithm (BA) in which a local search procedure performed by five different neighborhood structures is developed. Moreover, a self-adaptive (SA) tuning strategy to preserve the swarm diversity is implemented. The effectiveness of the proposed SA-BA is investigated by an experiment conducted on 21 benchmark problems that are well known in the literature. Findings Computational results indicate that the proposed SA-BA algorithm is computationally efficient through comparison with other existing algorithms found from the literature according to solution quality. As for the actual computational time, the SA-BA algorithm outperforms others. However, the scaled computational time of the SA-BA algorithm underperforms the other algorithms. Originality/value In this work the authors show that the proposed SA-BA is effective as a method for the TTRP problem. To the authors’ knowledge, the BA has not been applied previously, as in this work, to solve the TTRP problem.

scholarly journals Improved invasive weed optimization algorithm (IWO) based on chaos theory for optimal design of PID controller

2019 ◽  
Vol 6 (3) ◽  
pp. 284-295 ◽  
Author(s):  
Mojgan Misaghi ◽  
Mahdi Yaghoobi
Keyword(s):  
Standard Deviation ◽  
Chaos Theory ◽  
Optimization Algorithm ◽  
Pid Controller ◽  
Optimization Problems ◽  
Optimization Method ◽  
Population Based ◽  
Invasive Weed Optimization ◽  
Invasive Weed ◽  
Chaotic Mapping

Abstract Weed is a phenomenon which is looks for optimality and finds the best environment for life and quickly adapts itself to environmental conditions and resists changes. Considering these features, a powerful optimization algorithm is developed in this study. The invasive weed optimization algorithm (IWO) is a population-based evolutionary optimization method inspired by the behavior of weed colonies. In this paper, the IWO algorithm is based on chaos theory. Among parameters of weed optimization algorithm, standard deviation affects the performance of the algorithm significantly. Therefore, chaotic maps are used in the standard deviation parameter. Performance of the chaotic invasive weed development method is investigated on five benchmark functions, using logistic chaotic mapping. Additionally, the problem of setting the PID controller parameters for a DC motor using the proposed method is discussed. The statistical results on optimization problems show that the improved chaotic weed algorithm has gained fast convergence rate and high accuracy. Highlights Improved Invasive weed optimization Algorithm (IWO) based on Chaos theory. Improved setting the parameters of PID controller uses Chaotic IWO Algorithm.

scholarly journals A Comparative Study of EAG and PBIL on Large-Scale Global Optimization Problems

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Imtiaz Hussain Khan
Keyword(s):  
Global Optimization ◽  
Comparative Study ◽  
Large Scale ◽  
Optimization Problems ◽  
Computational Cost ◽  
Population Based ◽  
Statistical Information ◽  
Location Information ◽  
Solution Quality ◽  
Estimation Of Distribution

Estimation of Distribution Algorithms (EDAs) use global statistical information effectively to sample offspring disregarding the location information of the locally optimal solutions found so far. Evolutionary Algorithm with Guided Mutation (EAG) combines global statistical information and location information to sample offspring, aiming that this hybridization improves the search and optimization process. This paper discusses a comparative study of Population-Based Incremental Learning (PBIL), a representative of EDAs, and EAG on large-scale global optimization problems. We implemented PBIL and EAG to build an experimental setup upon which simulations were run. The performance of these algorithms was analyzed in terms of solution quality and computational cost. We found that EAG performed better than PBIL in attaining a good quality solution, but the latter performed better in terms of computational cost. We also compared the performance of EAG and PBIL with MA-SW-Chains, the winner of CEC’2010, and found that the overall performance of EAG is comparable to MA-SW-Chains.

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