Multiobjective Optimization of Bioethanol Production via Hydrolysis Using Hopfield-Enhanced Differential Evolution

2014 ◽  
pp. 340-359 ◽  
Author(s):  
T. Ganesan ◽  
I. Elamvazuthi ◽  
K. Z. K. Shaari ◽  
P. Vasant
Keyword(s):  
Differential Evolution ◽  
Multiple Solutions ◽  
Heuristic Algorithms ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Pareto Frontier ◽  
Weighted Sum ◽  
Bioethanol Production ◽  
Hypervolume Indicator

Many industrial problems in process optimization are Multi-Objective (MO), where each of the objectives represents different facets of the issue. Thus, having in hand multiple solutions prior to selecting the best solution is a seminal advantage. In this chapter, the weighted sum scalarization approach is used in conjunction with three meta-heuristic algorithms: Differential Evolution (DE), Hopfield-Enhanced Differential Evolution (HEDE), and Gravitational Search Algorithm (GSA). These methods are then employed to trace the approximate Pareto frontier to the bioethanol production problem. The Hypervolume Indicator (HVI) is applied to gauge the capabilities of each algorithm in approximating the Pareto frontier. Some comparative studies are then carried out with the algorithms developed in this chapter. Analysis on the performance as well as the quality of the solutions obtained by these algorithms is shown here.

Comparison of Meta-heuristic Algorithms on Benchmark Functions

2019 ◽  
Vol 2 (3) ◽  
pp. 508-517
Author(s):  
FerdaNur Arıcı ◽  
Ersin Kaya
Keyword(s):  
Heuristic Algorithms ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Optimization Algorithms ◽  
Gravitational Search Algorithm ◽  
Differential Evolution Algorithm ◽  
Population Based ◽  
Time Interval ◽  
Bee Colony ◽  
Different Characteristics

Optimization is a process to search the most suitable solution for a problem within an acceptable time interval. The algorithms that solve the optimization problems are called as optimization algorithms. In the literature, there are many optimization algorithms with different characteristics. The optimization algorithms can exhibit different behaviors depending on the size, characteristics and complexity of the optimization problem. In this study, six well-known population based optimization algorithms (artificial algae algorithm - AAA, artificial bee colony algorithm - ABC, differential evolution algorithm - DE, genetic algorithm - GA, gravitational search algorithm - GSA and particle swarm optimization - PSO) were used. These six algorithms were performed on the CEC’17 test functions. According to the experimental results, the algorithms were compared and performances of the algorithms were evaluated.

scholarly journals Optimized Wireless Sensor Node Multidimensional Routing using Fuzzy Clustering and Chaotic Gravitational Search Algorithm

2021 ◽  
Vol 3 (1) ◽  
pp. 40-48
Author(s):  
Sivaganesan D
Keyword(s):  
Sensor Node ◽  
Heuristic Algorithms ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Base Station ◽  
Wireless Sensor ◽  
Destination Node ◽  
Wireless Sensor Node ◽  
Search Spaces ◽  
Gravitational Search

A network of tiny sensors located at various regions for sensing and transmitting information is termed as wireless sensor networks. The information from multiple network nodes reach the destination node or the base station where data processing is performed. In larger search spaces, the clustering mechanisms and routing solutions provided by the existing heuristic algorithms are often inefficient. The sensor node resources are depleted by un-optimized processes created by reduced routing and clustering optimization levels in large search spaces. Chaotic Gravitational Search Algorithm and Fuzzy based clustering schemes are used to overcome the limitations and challenges of the conventional routing systems. This enables effective routing and efficient clustering in large search spaces. In each cluster, among the available nodes, appropriate node is selected as the cluster head. Reduction in delay, increase in energy consumption, increase in network lifetime and improvement of the network clustering accuracy are evident from the simulation results.

scholarly journals Effective Heuristic Algorithms Solving the Jobshop Scheduling Problem with Release Dates

Mathematics ◽  
2020 ◽  
Vol 8 (8) ◽  
pp. 1221
Author(s):  
Tao Ren ◽  
Yan Zhang ◽  
Shuenn-Ren Cheng ◽  
Chin-Chia Wu ◽  
Meng Zhang ◽  
...  
Keyword(s):  
Differential Evolution ◽  
Large Scale ◽  
Manufacturing Industry ◽  
Heuristic Algorithms ◽  
Search Algorithm ◽  
Optimal Schedule ◽  
Differential Evolution Algorithm ◽  
Developed Countries ◽  
Productivity Level ◽  
Jobshop Scheduling

Manufacturing industry reflects a country’s productivity level and occupies an important share in the national economy of developed countries in the world. Jobshop scheduling (JSS) model originates from modern manufacturing, in which a number of tasks are executed individually on a series of processors following their preset processing routes. This study addresses a JSS problem with the criterion of minimizing total quadratic completion time (TQCT), where each task is available at its own release date. Constructive heuristic and meta-heuristic algorithms are introduced to handle different scale instances as the problem is NP-hard. Given that the shortest-processing-time (SPT)-based heuristic and dense scheduling rule are effective for the TQCT criterion and the JSS problem, respectively, an innovative heuristic combining SPT and dense scheduling rule is put forward to provide feasible solutions for large-scale instances. A preemptive single-machine-based lower bound is designed to estimate the optimal schedule and reveal the performance of the heuristic. Differential evolution algorithm is a global search algorithm on the basis of population, which has the advantages of simple structure, strong robustness, fast convergence, and easy implementation. Therefore, a hybrid discrete differential evolution (HDDE) algorithm is presented to obtain near-optimal solutions for medium-scale instances, where multi-point insertion and a local search scheme enhance the quality of final solutions. The superiority of the HDDE algorithm is highlighted by contrast experiments with population-based meta-heuristics, i.e., ant colony optimization (ACO), particle swarm optimization (PSO) and genetic algorithm (GA). Average gaps 45.62, 63.38 and 188.46 between HDDE with ACO, PSO and GA, respectively, are demonstrated by the numerical results with benchmark data, which reveals the domination of the proposed HDDE algorithm.

scholarly journals Inverse calculation of demolition robot based on gravitational search algorithm and differential evolution neural network

2020 ◽  
Vol 17 (3) ◽  
pp. 172988142092529
Author(s):  
Jianzhong Huang ◽  
Yuwan Cen ◽  
Nenggang Xie ◽  
Xiaohua Ye
Keyword(s):  
Neural Network ◽  
Convergence Rate ◽  
Differential Evolution ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Population Diversity ◽  
Measuring Point ◽  
Mapping Model ◽  
Gravitational Search ◽  
Set Up

For the inverse calculation of laser-guided demolition robot, its global nonlinear mapping model from laser measuring point to joint cylinder stroke has been set up with an artificial neural network. Due to the contradiction between population diversity and convergence rate in the optimization of complex neural networks by using differential evolution, a gravitational search algorithm and differential evolution is proposed to accelerate the convergence rate of differential evolution population driven by gravity. Gravitational search algorithm and differential evolution is applied to optimize the inverse calculation neural network mapping model of demolition robot, and the algorithm simulation shows that gravity can effectively regulate the convergence process of differential evolution population. Compared with the standard differential evolution, the convergence speed and accuracy of gravitational search algorithm and differential evolution are significantly improved, which has better optimization stability. The calculation results show that the output accuracy of this gravitational and differential evolution neural network can meet the calculation requirements of the positioning control of demolition robot’s manipulator. The optimization using gravitational search algorithm and differential evolution is done with the connection weights of a neural network in this article, and as similar techniques can be applied to the other hyperparameter optimization problem. Moreover, such an inverse calculation method can provide a reference for the autonomous positioning of large hydraulic series manipulator, so as to improve the robotization level of construction machinery.

Generation control of isolated small hydro power plant by using DECSA optimized FPI controller

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Jyoti Ranjan Nayak ◽  
Binod Shaw ◽  
Neeraj Kumar Dewangan
Keyword(s):  
Comparative Analysis ◽  
Differential Evolution ◽  
Search Algorithm ◽  
Robustness Analysis ◽  
Content Type ◽  
Statistical Measures ◽  
Speed Governor ◽  
Terminal Voltage ◽  
Generation Control

Purpose In this work, generation control of an isolated small hydro plant (SHP) is demonstrated by applying optimal controllers in speed governor and hydraulic turbine system. A comparative analysis of application of fuzzy PI (FPI) and PID controller is conferred for generation control (both power and terminal voltage) of an SHP. The controllers are designed optimally by using crow search algorithm (CSA) and novel hybrid differential evolution crow search algorithm (DECSA). The purpose of this paper is to settle the voltage and real power to improve the quality of the power. Design/methodology/approach In this work, the controllers (PID and FPI) are implemented in speed governor and excitation system of SHP to regulate power and terminal voltage. Differential evolution and CSA are hybridized to enhance the performance of controller to refurbish the power and terminal voltage of SHP. Findings The proposed DECSA algorithm is applied to solve ten benchmark functions, and the effectiveness of DECSA algorithm over CSA and DE is demonstrated in terms of best value, mean and standard deviation. CSA and DECSA algorithms optimized controllers (PID and FPI) are used to design SHP with the capability to contribute power and voltage of better quality. The comparative analysis to substantiate the competence of DECSA algorithm and FPI controller is demonstrated in terms of statistical measures of power and voltage of SHP. Robustness analysis is performed by varying all system parameters to prove the effectiveness of the proposed controller. Originality/value The proposed algorithm and FPI controller are applied individually to improve the quality of the power of SHP. DE, CSA and DECSA algorithms are implemented to solve benchmark equations. The solutions of all benchmark equations contributed by DECSA algorithm is converged rapidly and having minimum statistical measures as compared to DE and CSA algorithms. The DECSA algorithm and FPI controller are proposed with superior competence to enhance the generator performances by conceding undershoot, overshoot and settling time of power and terminal voltage. DECSA-based FPI controller contributes a noticeable improvement of the performances over other approaches.

Meta-Heuristic Structure for Multiobjective Optimization Case Study

2014 ◽  
pp. 38-58
Author(s):  
T. Ganesan ◽  
I. Elamvazuthi ◽  
K. Z. KuShaari ◽  
P. Vasant
Keyword(s):  
Performance Metrics ◽  
Heuristic Algorithms ◽  
Weighted Sum ◽  
Optimal Solutions ◽  
Green Sand ◽  
Weighted Sum Method ◽  
Real World Problem ◽  
Green Sand Mould System

In engineering optimization, one often encounters scenarios that are multiobjective (MO) where each of the objectives covers different aspects of the problem. It is hence critical for the engineer to have multiple solution choices before selecting of the best solution. In this chapter, an approach that merges meta-heuristic algorithms with the weighted sum method is introduced. Analysis on the solution set produced by these algorithms is carried out using performance metrics. By these procedures, a novel chaos-based metaheuristic algorithm, the Chaotic Particle Swarm (Ch-PSO) is developed. This method is then used generate highly diverse and optimal solutions to the green sand mould system which is a real-world problem. Some comparative analyses are then carried out with the algorithms developed and employed in this work. Analysis on the performance as well as the quality of the solutions produced by the algorithms is presented in this chapter.

A novel optimization-based method to find multiple solutions for path synthesis of planar four-bar and slider-crank mechanisms

2021 ◽  
pp. 095440622098336
Author(s):  
Soheil Zarkandi
Keyword(s):  
Feasible Solution ◽  
Heuristic Algorithms ◽  
Classical Method ◽  
Search Algorithm ◽  
Error Function ◽  
Continuation Method ◽  
Gravitational Search Algorithm ◽  
Planar Mechanisms ◽  
All Solutions ◽  
Path Synthesis

The classical method to find possible solutions for path synthesis problem of planar mechanisms is continuation method. However, this method has some disadvantages such as producing unwanted extraneous and degenerate solutions and also producing mechanisms having defects. Moreover, many of the solutions are cognate of each other which can be obtained geometrically. Thus, finding the most feasible solution among all solutions is a cumbersome and time-consuming task. The main purpose of this paper is to explore applicability of heuristic algorithms to find multiple cognate- and defect-free solutions for path synthesis problems of planar four-bar and slider-crank mechanisms. To this aim, a new modified error function and an optimization-based algorithm is presented. The gravitational search algorithm (GSA) is utilized to minimize the modified error function. Efficiency of the method is proved through five case studies for path synthesis of four-bar and slider-crank mechanisms with and without prescribed timing.

scholarly journals Hybridization of Chaotic Maps and Gravitational Search Algorithm for Constrained Mechanical and Civil Engineering Design Frameworks

2022 ◽  
Vol 13 (1) ◽  
pp. 0-0
Keyword(s):  
Engineering Design ◽  
Chaotic Maps ◽  
Civil Engineering ◽  
Heuristic Algorithms ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Gear Train ◽  
Significance Level ◽  
Beam Design ◽  
Gravitational Search

The Chaotic Gravitational Search Algorithm (CGSA) is a physics-based heuristic algorithm inspired by Newton's law of universal gravitation. It uses 10 chaotic maps for optimal global search and fast convergence rate. The advantages of CGSA has been incorporated in various Mechanical and Civil engineering design frameworks which include Speed Reducer Design (SRD), Gear Train Design (GTD), Three Bar Truss Design (TBTD), Stepped Cantilever Beam Design (SCBD), Multiple Disc Clutch Brake Design (MDCBD), and Hydrodynamic Thrust Bearing Design (HTBD). The CGSA has been compared with eleven state of the art stochastic algorithms. In addition, a non-parametric statistical test namely the Signed Wilcoxon Rank-Sum test has been carried out at a 5% significance level to statistically validate the results. The simulation results indicate that CGSA shows efficient performance in terms of high convergence speed and minimization of the design parameter values as compared to other heuristic algorithms. The source codes are publicly available on Github i.e. https://github.com/SajadAHMAD1.

Inspirations from Nature for Meta-heuristic Algorithms: A Survey

2019 ◽  
Vol 13 ◽  
Author(s):  
Rohit Kumar Sachan ◽  
Dharmender Singh Kushwaha
Keyword(s):  
Heuristic Algorithms ◽  
Optimization Problems ◽  
Water Cycle ◽  
Search Algorithm ◽  
Theoretical Perspective ◽  
Gravitational Search Algorithm ◽  
Natural Phenomenon ◽  
Flower Pollination Algorithm ◽  
Shuffled Frog Leaping ◽  
Nature Inspired Algorithms

Background: Nature-Inspired Algorithms (NIAs) are the most efficient way to solve advanced engineering and real-world optimization problems. Since the last few decades, various researchers have proposed an immense number of NIAs. These NIAs get inspiration from natural phenomenon. A young researcher attempting to undertake or solve a problem using NIAs is bogged down by a plethora of proposals that exist today. Not every algorithm is suited for all kinds of problem. Some scores over others. Objective: This paper presents a comprehensive study of seven NIAs, which have new and unique inspirations. This study shall useful to easily understand the fundamentals of NIAs for any new entrant. Conclusion: Here, we classify the NIAs as natural evolution based, swarm intelligence based, biological based, science based and others. In this survey, well-establish and relatively new NIAs, namely- Shuffled Frog Leaping Algorithm (SFLA), Firefly Algorithm (FA), Gravitational Search Algorithm (GSA), Flower Pollination Algorithm (FPA), Water Cycle Algorithm (WCA), Jaya Algorithm and Anti-Predatory NIA (APNIA), have been studied. This study presents a theoretical perspective of NIAs in a simplified form based on its source of inspiration, mathematical formulations, control parameters, features, variants and area of application, where these algorithms have been successfully applied.

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