Different Meta-Heuristic Optimization Techniques and Their Application in Solar Photovoltaic Field

2022 ◽  
pp. 1-37
Author(s):  
Krupali Devendra Kanekar ◽  
Rahul Agrawal ◽  
Dhiraj Magare
Keyword(s):  
Optimization Problems ◽  
Optimization Techniques ◽  
Heuristic Optimization ◽  
Solar Photovoltaic ◽  
Multimodal Approach ◽  
Objective Functions ◽  
Linear Type ◽  
Intelligent Technique

A method of optimization is used to resolve issues smartly by selecting the better option from various existing possibilities. Many optimization problems are possessing characteristics, namely nonlinearity, complexity, multimodal approach, and incompatible objective functions. Sometimes even for individual simple and linear type objective functions, a solution that is optimal and does not exist, there is uncertainness of obtaining the best solution. The aim of finding methods that can resolve various issues in a defined manner potentially has found the concentration of different researchers responsible for performing the advancement of a new “intelligent” technique called meta-heuristics technique. In the last few years, there is an advancement of various meta-heuristics techniques in different areas or various fields. Meta-heuristics are a demanded thrust stream of research that showed important advancement in finding the answer to problems that are optimized. The chapter gives the guidance for enhancing research more meaningfully.

scholarly journals Investigation of the iCC Framework Performance for Solving Constrained LSGO Problems

Algorithms ◽  
2020 ◽  
Vol 13 (5) ◽  
pp. 108
Author(s):  
Alexey Vakhnin ◽  
Evgenii Sopov
Keyword(s):  
Large Scale ◽  
Optimization Problems ◽  
Optimization Techniques ◽  
Objective Functions ◽  
Parameter Adaptation ◽  
New Approach ◽  
Scientific Papers ◽  
Evolution Algorithms ◽  
Low Dimensional ◽  
Modern Optimization

Many modern real-valued optimization tasks use “black-box” (BB) models for evaluating objective functions and they are high-dimensional and constrained. Using common classifications, we can identify them as constrained large-scale global optimization (cLSGO) tasks. Today, the IEEE Congress of Evolutionary Computation provides a special session and several benchmarks for LSGO. At the same time, cLSGO problems are not well studied yet. The majority of modern optimization techniques demonstrate insufficient performance when confronted with cLSGO tasks. The effectiveness of evolution algorithms (EAs) in solving constrained low-dimensional optimization problems has been proven in many scientific papers and studies. Moreover, the cooperative coevolution (CC) framework has been successfully applied for EA used to solve LSGO problems. In this paper, a new approach for solving cLSGO has been proposed. This approach is based on CC and a method that increases the size of groups of variables at the decomposition stage (iCC) when solving cLSGO tasks. A new algorithm has been proposed, which combined the success-history based parameter adaptation for differential evolution (SHADE) optimizer, iCC, and the ε-constrained method (namely ε-iCC-SHADE). We investigated the performance of the ε-iCC-SHADE and compared it with the previously proposed ε-CC-SHADE algorithm on scalable problems from the IEEE CEC 2017 Competition on constrained real-parameter optimization.

scholarly journals Estimation of Nonlinear Parameters of Type 6 Hydrological Method in Flood Routing With the Spotted Hyena Optimizer Algorithm (SHO)

2021 ◽  
Author(s):  
Saeid Khalifeh ◽  
Kazem Esmaili ◽  
S. Reza Khodashenas ◽  
Fereshteh Modaresi
Keyword(s):  
Optimization Problems ◽  
Flood Routing ◽  
Spotted Hyena ◽  
Objective Functions ◽  
Linear Type ◽  
Nonlinear Parameters ◽  
Muskingum Model ◽  
River Flood ◽  
Non Linear ◽  
Optimal Values

Abstract In this study, The Spotted hyena optimizer Algorithm (SHO) is used to optimize the parameters of the Non-linear type 6 Muskingum model for flood routing. To evaluate the performance of the SHO in the Non-linear Muskingum models, The Wilson River and the Wye River are applied by many researchers for validation. Moreover, in these studies, the Non-linear Muskingum parameters were estimated by the SHO Algorithm. The SSQ and DPO were considered as objective functions between computed and observed data. According to the results of Wilson river flood, the values of these objective functions for the NL3 model are 128.781, and 0.92 m3/s, and for the NL6 model, are 3.20 and 0.027, respectively. The results of the Wye River flood with the SHO showed that the SSQ and DPO for the NL3 model are 34789.39 and 90.05, and for the NL6 model are 30812.07 and 72.35, respectively. The results show that the proposed algorithm can be applied confidently to estimate the parameter optimal values of the nonlinear Muskingum model. Moreover, this algorithm may be applicable to any continuous engineering optimization problems.

The Method of Alternate Formulations, an Automated Strategy for Optimal Design

1988 ◽  
Vol 110 (4) ◽  
pp. 459-463 ◽  
Author(s):  
C. R. Hammond ◽  
G. E. Johnson
Keyword(s):  
Optimal Design ◽  
Trend Analysis ◽  
Optimization Problems ◽  
Optimization Techniques ◽  
Global Information ◽  
Objective Functions ◽  
State Equations ◽  
Complete Set ◽  
Optimum Solution ◽  
Monotonicity Analysis

The Method of Alternate Formulations (MAF) is a new method of global information extraction for constrained, nonlinear optimization problems. MAF automatically generates the complete set of candidate solution points for these optimization problems using a symbolic mathematics computer package. MAF uses ideas and techniques from both the Method of Optimal Design and Monotonicity Analysis to reduce and formulate the problem. The reduced problem is repeatedly reformulated to develop state equations and objective functions in terms of all possible variable partitions. Trend analysis on the decision variables in the objective functions yields global information about constraint activity at possible solution points. Trend analysis on all of the possible formulations of the objective functions yields the complete set of candidate solutions. The state equations in each partition of the variables are used to test the feasibility of these candidate solutions, and the best feasible point is selected as the optimum solution. MAF can be used as a preprocessor for standard numerical optimization techniques and can be extended to nonmonotonic problems.

Scalable Computing Architecture for Time-Dependent Transportation Optimization Problems Based on High-Performance Computing Techniques

2019 ◽  
Vol 2673 (4) ◽  
pp. 205-216 ◽  
Author(s):  
Pengfei (Taylor) Li ◽  
Peirong (Slade) Wang ◽  
Farzana Chowdhury ◽  
Li Zhang
Keyword(s):  
Objective Function ◽  
High Performance Computing ◽  
High Performance ◽  
Optimization Problems ◽  
Dynamic Network ◽  
Alternative Formulation ◽  
High Fidelity ◽  
Objective Functions ◽  
Performance Computing ◽  
Transportation Optimization

Traditional formulations for transportation optimization problems mostly build complicating attributes into constraints while keeping the succinctness of objective functions. A popular solution is the Lagrangian decomposition by relaxing complicating constraints and then solving iteratively. Although this approach is effective for many problems, it generates intractability in other problems. To address this issue, this paper presents an alternative formulation for transportation optimization problems in which the complicating attributes of target problems are partially or entirely built into the objective function instead of into the constraints. Many mathematical complicating constraints in transportation problems can be efficiently modeled in dynamic network loading (DNL) models based on the demand–supply equilibrium, such as the various road or vehicle capacity constraints or “IF–THEN” type constraints. After “pre-building” complicating constraints into the objective functions, the objective function can be approximated well with customized high-fidelity DNL models. Three types of computing benefits can be achieved in the alternative formulation: ( a) the original problem will be kept the same; ( b) computing complexity of the new formulation may be significantly reduced because of the disappearance of hard constraints; ( c) efficiency loss on the objective function side can be mitigated via multiple high-performance computing techniques. Under this new framework, high-fidelity and problem-specific DNL models will be critical to maintain the attributes of original problems. Therefore, the authors’ recent efforts in enhancing the DNL’s fidelity and computing efficiency are also described in the second part of this paper. Finally, a demonstration case study is conducted to validate the new approach.

scholarly journals A Method for Integration of Preferences to a Multi-Objective Evolutionary Algorithm Using Ordinal Multi-Criteria Classification

2021 ◽  
Vol 26 (2) ◽  
pp. 27
Author(s):  
Alejandro Castellanos-Alvarez ◽  
Laura Cruz-Reyes ◽  
Eduardo Fernandez ◽  
Nelson Rangel-Valdez ◽  
Claudia Gómez-Santillán ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Selective Pressure ◽  
Optimization Problems ◽  
Region Of Interest ◽  
Decision Makers ◽  
Multiple Objective ◽  
Objective Functions ◽  
Multi Objective ◽  
Imperfect Knowledge ◽  
Real World Problems

Most real-world problems require the optimization of multiple objective functions simultaneously, which can conflict with each other. The environment of these problems usually involves imprecise information derived from inaccurate measurements or the variability in decision-makers’ (DMs’) judgments and beliefs, which can lead to unsatisfactory solutions. The imperfect knowledge can be present either in objective functions, restrictions, or decision-maker’s preferences. These optimization problems have been solved using various techniques such as multi-objective evolutionary algorithms (MOEAs). This paper proposes a new MOEA called NSGA-III-P (non-nominated sorting genetic algorithm III with preferences). The main characteristic of NSGA-III-P is an ordinal multi-criteria classification method for preference integration to guide the algorithm to the region of interest given by the decision-maker’s preferences. Besides, the use of interval analysis allows the expression of preferences with imprecision. The experiments contrasted several versions of the proposed method with the original NSGA-III to analyze different selective pressure induced by the DM’s preferences. In these experiments, the algorithms solved three-objectives instances of the DTLZ problem. The obtained results showed a better approximation to the region of interest for a DM when its preferences are considered.

scholarly journals Joint Optimization on Trajectory, Cache Placement, and Transmission Power for Minimum Mission Time in UAV-Aided Wireless Networks

2021 ◽  
Vol 10 (7) ◽  
pp. 426
Author(s):  
Tingting Lan ◽  
Danyang Qin ◽  
Guanyu Sun
Keyword(s):  
Wireless Communication ◽  
Power Allocation ◽  
Optimization Problems ◽  
Base Station ◽  
Optimization Techniques ◽  
Joint Optimization ◽  
Transmission Power ◽  
Wireless Backhaul ◽  
Mission Time ◽  
Cache Placement

In recent years, due to the strong mobility, easy deployment, and low cost of unmanned aerial vehicles (UAV), great interest has arisen in utilizing UAVs to assist in wireless communication, especially for on-demand deployment in emergency situations and temporary events. However, UAVs can only provide users with data transmission services through wireless backhaul links established with a ground base station, and the limited capacity of the wireless backhaul link would limit the transmission speed of UAVs. Therefore, this paper designed a UAV-assisted wireless communication system that used cache technology and realized the transmission of multi-user data by using the mobility of UAVs and wireless cache technology. Considering the limited storage space and energy of UAVs, the joint optimization problem of the UAV’s trajectory, cache placement, and transmission power was established to minimize the mission time of the UAV. Since this problem was a non-convex problem, it was decomposed into three sub-problems: trajectory optimization, cache placement optimization, and power allocation optimization. An iterative algorithm based on the successive convex approximation and alternate optimization techniques was proposed to solve these three optimization problems. Finally, in the power allocation optimization, the proposed algorithm was improved by changing the optimization objective function. Numerical results showed that the algorithm had good performance and could effectively reduce the task completion time of the UAV.

scholarly journals Optimal control of the Sabatier process in microchannel reactors

2021 ◽  
Vol 128 (1) ◽  
Author(s):  
Sebastian Blauth ◽  
Christian Leithäuser ◽  
René Pinnau
Keyword(s):  
Flow Rate ◽  
Wall Temperature ◽  
Optimization Problems ◽  
Product Yield ◽  
Identification Problem ◽  
State Constraint ◽  
Optimization Techniques ◽  
Reacting Flow ◽  
Microchannel Reactor ◽  
Highly Nonlinear

AbstractWe consider the optimization of a chemical microchannel reactor by means of PDE-constrained optimization techniques, using the example of the Sabatier reaction. To model the chemically reacting flow in the microchannels, we introduce a three- and a one-dimensional model. As these are given by strongly coupled and highly nonlinear systems of partial differential equations (PDEs), we present our software package cashocs which implements the adjoint approach and facilitates the numerical solution of the subsequent optimization problems. We solve a parameter identification problem numerically to determine necessary kinetic parameters for the models from experimental data given in the literature. The obtained results show excellent agreement to the measurements. Finally, we present two optimization problems for optimizing the reactor’s product yield. First, we use a tracking-type cost functional to maximize the reactant conversion, keep the flow rate of the reactor fixed, and use its wall temperature as optimization variable. Second, we consider the wall temperature and the inlet gas velocity as optimization variables, use an objective functional for maximizing the flow rate in the reactor, and ensure the quality of the product by means of a state constraint. The results obtained from solving these problems numerically show great potential for improving the design of the microreactor.

Assessing the performances of recent global search algorithms using analytic objective functions and seismic optimization problems

Geophysics ◽  
2019 ◽  
Vol 84 (5) ◽  
pp. R767-R781 ◽  
Author(s):  
Mattia Aleardi ◽  
Silvio Pierini ◽  
Angelo Sajeva
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Model Space ◽  
Global Search ◽  
Objective Functions ◽  
Swarm Optimization ◽  
Fireworks Algorithm ◽  
Highly Nonlinear ◽  
Whale Optimization

We have compared the performances of six recently developed global optimization algorithms: imperialist competitive algorithm, firefly algorithm (FA), water cycle algorithm (WCA), whale optimization algorithm (WOA), fireworks algorithm (FWA), and quantum particle swarm optimization (QPSO). These methods have been introduced in the past few years and have found very limited or no applications to geophysical exploration problems thus far. We benchmark the algorithms’ results against the particle swarm optimization (PSO), which is a popular and well-established global search method. In particular, we are interested in assessing the exploration and exploitation capabilities of each method as the dimension of the model space increases. First, we test the different algorithms on two multiminima and two convex analytic objective functions. Then, we compare them using the residual statics corrections and 1D elastic full-waveform inversion, which are highly nonlinear geophysical optimization problems. Our results demonstrate that FA, FWA, and WOA are characterized by optimal exploration capabilities because they outperform the other approaches in the case of optimization problems with multiminima objective functions. Differently, QPSO and PSO have good exploitation capabilities because they easily solve ill-conditioned optimizations characterized by a nearly flat valley in the objective function. QPSO, PSO, and WCA offer a good compromise between exploitation and exploration.

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