scholarly journals Mendelian Evolutionary Theory Optimization Algorithm

2020 ◽  
Author(s):  
Neeraj Gupta ◽  
Mahdi Khosravy ◽  
Nilesh Patel ◽  
Nilanjan Dey ◽  
Om Prakash Mahela
Keyword(s):  
Evolutionary Theory ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Bat Algorithm ◽  
Cuckoo Search ◽  
Invasive Weed ◽  
Covariance Matrix Adaptation ◽  
Successive Generations ◽  
Evolutionary Operators ◽  
Benchmark Test Functions

<div>This study presented a new multi-species binary coded algorithm, Mendelian Evolutionary Theory Optimization (METO), inspired by the plant genetics. This framework mainly consists of three concepts: First, the “denaturation” of DNA’s of two different species to produce the hybrid “offspring DNA”. Second , the Mendelian evolutionary theory of genetic inheritance, which explains how the dominant and recessive traits appear in two successive generations. Third, the Epimuation, through which organism resist for natural mutation. The above concepts are reconfigured in order to design the binary meta-heuristic evolutionary search technique. Based on this framework, four evolutionary operators – 1) Flipper, 2) Pollination, 3) Breeding, and 4) Epimutation – are created in the binary domain. In this paper, METO is compared with well-known evolutionary and swarm optimizers 1) Binary Hybrid GA (BHGA), 2) Bio-geography Based Optimization (BBO), 3) Invasive Weed Optimization (IWO), 4) Shuffled Frog Leap Algorithm (SFLA), 5) Teaching-Learning Based Optimization (TLBO), 6) Cuckoo Search (CS), 7) Bat Algorithm (BA), 8) Gravitational Search Algorithm (GSA), 9) Covariance Matrix Adaptation Evolution Strategy(CMAES), 10) Differential Evolution (DE), 11) Firefly Algorithm (FA) and 12) Social Learning PSO (SLPSO). This comparison is evaluated on 30 and 100 variables benchmark test functions, including noisy, rotated, and hybrid composite functions. Kruskal Wallis statistical rank-based non-parametric H-test is utilized to determine the statistically significant differences between the output distributions of the optimizer, which are the result of the 100 independent runs. The statistical analysis shows that METO is a significantly better algorithm for complex and multi-modal problems with many local extremes.</div>

scholarly journals Mendelian Evolutionary Theory Optimization Algorithm

2020 ◽  
Author(s):  
Neeraj Gupta ◽  
Mahdi Khosravy ◽  
Nilesh Patel ◽  
Nilanjan Dey ◽  
Om Prakash Mahela
Keyword(s):  
Evolutionary Theory ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Bat Algorithm ◽  
Cuckoo Search ◽  
Invasive Weed ◽  
Covariance Matrix Adaptation ◽  
Successive Generations ◽  
Evolutionary Operators ◽  
Benchmark Test Functions

<div>This study presented a new multi-species binary coded algorithm, Mendelian Evolutionary Theory Optimization (METO), inspired by the plant genetics. This framework mainly consists of three concepts: First, the “denaturation” of DNA’s of two different species to produce the hybrid “offspring DNA”. Second , the Mendelian evolutionary theory of genetic inheritance, which explains how the dominant and recessive traits appear in two successive generations. Third, the Epimuation, through which organism resist for natural mutation. The above concepts are reconfigured in order to design the binary meta-heuristic evolutionary search technique. Based on this framework, four evolutionary operators – 1) Flipper, 2) Pollination, 3) Breeding, and 4) Epimutation – are created in the binary domain. In this paper, METO is compared with well-known evolutionary and swarm optimizers 1) Binary Hybrid GA (BHGA), 2) Bio-geography Based Optimization (BBO), 3) Invasive Weed Optimization (IWO), 4) Shuffled Frog Leap Algorithm (SFLA), 5) Teaching-Learning Based Optimization (TLBO), 6) Cuckoo Search (CS), 7) Bat Algorithm (BA), 8) Gravitational Search Algorithm (GSA), 9) Covariance Matrix Adaptation Evolution Strategy(CMAES), 10) Differential Evolution (DE), 11) Firefly Algorithm (FA) and 12) Social Learning PSO (SLPSO). This comparison is evaluated on 30 and 100 variables benchmark test functions, including noisy, rotated, and hybrid composite functions. Kruskal Wallis statistical rank-based non-parametric H-test is utilized to determine the statistically significant differences between the output distributions of the optimizer, which are the result of the 100 independent runs. The statistical analysis shows that METO is a significantly better algorithm for complex and multi-modal problems with many local extremes.</div>

scholarly journals Binary Spring Search Algorithm for Solving Various Optimization Problems

2021 ◽  
Vol 11 (3) ◽  
pp. 1286 ◽  
Author(s):  
Mohammad Dehghani ◽  
Zeinab Montazeri ◽  
Ali Dehghani ◽  
Om P. Malik ◽  
Ruben Morales-Menendez ◽  
...  
Keyword(s):  
Optimization Algorithm ◽  
High Performance ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Bat Algorithm ◽  
Search Space ◽  
Binary Particle Swarm Optimization ◽  
Dragonfly Algorithm ◽  
Grasshopper Optimization Algorithm

One of the most powerful tools for solving optimization problems is optimization algorithms (inspired by nature) based on populations. These algorithms provide a solution to a problem by randomly searching in the search space. The design’s central idea is derived from various natural phenomena, the behavior and living conditions of living organisms, laws of physics, etc. A new population-based optimization algorithm called the Binary Spring Search Algorithm (BSSA) is introduced to solve optimization problems. BSSA is an algorithm based on a simulation of the famous Hooke’s law (physics) for the traditional weights and springs system. In this proposal, the population comprises weights that are connected by unique springs. The mathematical modeling of the proposed algorithm is presented to be used to achieve solutions to optimization problems. The results were thoroughly validated in different unimodal and multimodal functions; additionally, the BSSA was compared with high-performance algorithms: binary grasshopper optimization algorithm, binary dragonfly algorithm, binary bat algorithm, binary gravitational search algorithm, binary particle swarm optimization, and binary genetic algorithm. The results show the superiority of the BSSA. The results of the Friedman test corroborate that the BSSA is more competitive.

scholarly journals A Multi-Objective Optimization Dispatch Method for Microgrid Energy Management Considering the Power Loss of Converters

Energies ◽  
2019 ◽  
Vol 12 (11) ◽  
pp. 2160 ◽  
Author(s):  
Xiaomin Wu ◽  
Weihua Cao ◽  
Dianhong Wang ◽  
Min Ding
Keyword(s):  
Power Loss ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Inertial Mass ◽  
Real Time Scheduling ◽  
Opposition Based Learning ◽  
Hybrid Particle Swarm Optimization ◽  
Time Scheduling ◽  
Benchmark Test Functions ◽  
Energy Conversion Devices

With the spreading and applying of microgrids, the economic and environment friendly microgrid operations are required eagerly. For the dispatch of practical microgrids, power loss from energy conversion devices should be considered to improve the efficiency. This paper presents a two-stage dispatch (TSD) model based on the day-ahead scheduling and the real-time scheduling to optimize dispatch of microgrids. The power loss cost of conversion devices is considered as one of the optimization objectives in order to reduce the total cost of microgrid operations and improve the utility efficiency of renewable energy. A hybrid particle swarm optimization and opposition-based learning gravitational search algorithm (PSO-OGSA) is proposed to solve the optimization problem considering various constraints. Some improvements of PSO-OGSA, such as the distribution optimization of initial populations, the improved inertial mass update rule, and the acceleration mechanism combining the memory and community of PSO, have been integrated into the proposed approach to obtain the best solution for the optimization dispatch problem. The simulation results for several benchmark test functions and an actual test microgrid are employed to show the effectiveness and validity of the proposed model and algorithm.

Multiloop FOPID Controller Design for TITO Process Using Evolutionary Algorithm

2019 ◽  
Vol 8 (3) ◽  
pp. 117-130 ◽  
Author(s):  
Lakshmanaprabu S.K. ◽  
Najumnissa Jamal D. ◽  
Sabura Banu U.
Keyword(s):  
Evolutionary Algorithm ◽  
Pid Controller ◽  
Controller Design ◽  
Search Algorithm ◽  
Bat Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Internal Model Control ◽  
Model Control ◽  
Fractional Order Pid

In this article, the tuning of multiloop Fractional Order PID (FOPID) controller is designed for Two Input Two Output (TITO) processes using an evolutionary algorithm such as the Genetic algorithm (GA), the Cuckoo Search algorithm (CS) and the Bat Algorithm (BA). The control parameters of FOPID are obtained using GA, CS, and BA for minimizing the integral error criteria. The main objective of this article is to compare the performance of the GA, CS, and BA for the multiloop FOPID controller problem. The integer order internal model control based PID (IMC-PID) controller is designed using the GA and the performance of the IMC-PID controller is compared with the FOPID controller scheme. The simulation results confirm that BA offers optimal controller parameter with a minimum value of IAE, ISE, ITAE with faster settling time.

A Survey of Computational Intelligence Algorithms and Their Applications

2020 ◽  
pp. 1886-1929
Author(s):  
Hadj Ahmed Bouarara
Keyword(s):  
Computational Intelligence ◽  
Search Algorithm ◽  
Bat Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Evolutionary Strategy ◽  
General Process ◽  
Fireworks Algorithm ◽  
Starting Point ◽  
New Algorithms

This chapter subscribes in the framework of an analytical study about the computational intelligence algorithms. These algorithms are numerous and can be classified in two great families: evolutionary algorithms (genetic algorithms, genetic programming, evolutionary strategy, differential evolutionary, paddy field algorithm) and swarm optimization algorithms (particle swarm optimisation PSO, ant colony optimization (ACO), bacteria foraging optimisation, wolf colony algorithm, fireworks algorithm, bat algorithm, cockroaches colony algorithm, social spiders algorithm, cuckoo search algorithm, wasp swarm optimisation, mosquito optimisation algorithm). We have detailed each algorithm following a structured organization (the origin of the algorithm, the inspiration source, the summary, and the general process). This paper is the fruit of many years of research in the form of synthesis which groups the contributions proposed by various researchers in this field. It can be the starting point for the designing and modelling new algorithms or improving existing algorithms.

Lévy-Enhanced Swarm Intelligence for Optimizing a Multiobjective Biofuel Supply Chain

2020 ◽  
pp. 287-309 ◽  
Author(s):  
T. Ganesan ◽  
Pandian Vasant
Keyword(s):  
Supply Chain ◽  
Swarm Intelligence ◽  
Large Scale ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Cuckoo Search ◽  
Biofuel Supply Chain ◽  
Solution Methods ◽  
The Face ◽  
Chain Problem

Engineering systems are currently plagued by various complexities and uncertainties. Metaheuristics have emerged as an essential tool for effective engineering design and operations. Nevertheless, conventional metaheuristics still struggle to reach optimality in the face of highly complex engineering problems. Aiming to further boost the performance of conventional metaheuristics, strategies such as hybridization and various enhancements have been added into the existing solution methods. In this work, swarm intelligence techniques were employed to solve the real-world, large-scale biofuel supply chain problem. Additionally, the supply chain problem considered in this chapter is multiobjective (MO) in nature. Comparative analysis was then performed on the swarm techniques. To further enhance the search capability of the best solution method (GSA), the Lévy flight component from the Cuckoo Search (CS) algorithm was incorporated into the Gravitational Search Algorithm (GSA) technique; developing the novel Lévy-GSA technique. Measurement metrics were then utilized to analyze the results.

Hybridizing cuckoo search algorithm with bat algorithm for global numerical optimization

2018 ◽  
Vol 75 (5) ◽  
pp. 2395-2422 ◽  
Author(s):  
Mohammad Shehab ◽  
Ahamad Tajudin Khader ◽  
Makhlouf Laouchedi ◽  
Osama Ahmad Alomari
Keyword(s):  
Numerical Optimization ◽  
Search Algorithm ◽  
Bat Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Global Numerical Optimization

The optimal design and analysis of wideband second-order microwave integrator

2019 ◽  
Vol 11 (3) ◽  
pp. 227-236
Author(s):  
Usha Gautam ◽  
Tarun Kumar Rawat
Keyword(s):  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Wide Band ◽  
Absolute Magnitude ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Performance Measure ◽  
Second Order ◽  
Magnitude Response ◽  
The Ideal

AbstractThe implementation of stable, accurate, and wideband second-order microwave integrators (SOMIs) is presented in this paper. These designs of SOMIs are obtained by using different combinations of transmission line sections and shunt stubs in cascading. Particle swarm optimization (PSO), cuckoo search algorithm (CSA), and gravitational search algorithm (GSA) are applied to obtain the optimal values of the characteristic impedances of these line elements to approximate the magnitude response of ideal second-order integrator (SOI). The performance measure criteria for the proposed SOMIs are carried out based on magnitude response, absolute magnitude error, phase response, convergence rate, pole-zero plot, and improvement graph. The simulation results and statistical analysis demonstrate that GSA surpasses the PSO and CSA to approximate the ideal SOI in all state-of-the-art, that is eligible for wide-band microwave integrator. The designed SOMI is compact in size and suitable to cover microwave applications. The magnitude errors for the proposed SOMIs GSA based are as low as 4.9954 and 3.6573, respectively. The structure of the designed SOMI is implemented in the form of microstrip line on RT/Duroid substrate with dielectric constant 2.2 and having height 0.762 mm. The simulated and measured magnitude result agrees well with the ideal one in the frequency range of 3–15 GHz.

A Nature-Inspired Metaheuristic Cuckoo Search Algorithm for Community Detection in Social Networks

2017 ◽  
Vol 8 (1) ◽  
pp. 50-62 ◽  
Author(s):  
Ramadan Babers ◽  
Aboul Ella Hassanien
Keyword(s):  
Social Networks ◽  
Community Detection ◽  
Optimization Algorithm ◽  
Search Algorithm ◽  
Bat Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Krill Herd ◽  
Artificial Fish Swarm ◽  
Lion Optimization Algorithm

In last few years many approaches have been proposed to detect communities in social networks using diverse ways. Community detection is one of the important researches in social networks and graph analysis. This paper presents a cuckoo search optimization algorithm with Lévy flight for community detection in social networks. Experimental on well-known benchmark data sets demonstrates that the proposed algorithm can define the structure and detect communities of complex networks with high accuracy and quality. In addition, the proposed algorithm is compared with some swarms algorithms including discrete bat algorithm, artificial fish swarm, discrete Krill Herd, ant lion algorithm and lion optimization algorithm and the results show that the proposed algorithm is competitive with these algorithms.

A New Strategy Based on GSABAT to Solve Single Objective Optimization Problem

2019 ◽  
Vol 10 (3) ◽  
pp. 1-22 ◽  
Author(s):  
H.A. Sattar ◽  
Alaa Cheetar ◽  
Iraq Tareq
Keyword(s):  
Optimization Problem ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Bat Algorithm ◽  
Local Optimum ◽  
Search Mechanism ◽  
All Solutions ◽  
New Strategy ◽  
Local Research ◽  
Single Objective

This article proposes a new strategy based on a hybrid method that combines the gravitational search algorithm (GSA) with the bat algorithm (BAT) to solve a single-objective optimization problem. It first runs GSA, followed by BAT as the second step. The proposed approach relies on a parameter between 0 and 1 to address the problem of falling into local research because the lack of a local search mechanism increases intensity search, whereas diversity remains high and easily falls into the local optimum. The improvement is equivalent to the speed of the original BAT. Access speed is increased for the best solution. All solutions in the population are updated before the end of the operation of the proposed algorithm. The diversification feature of BAT has solved the problem of weakness in diversity observed in the algorithm by applying the parameters used in BAT. Moreover, balance is achieved through the intensification properties of the algorithms.

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