scholarly journals Evolutionary algorithms, swarm intelligence methods, and their applications in water resources engineering: a state-of-the-art review

2020 ◽  
Vol 3 (1) ◽  
pp. 135-188 ◽  
Author(s):  
M. Janga Reddy ◽  
D. Nagesh Kumar
Keyword(s):  
Water Resources ◽  
Evolutionary Algorithms ◽  
Swarm Intelligence ◽  
Water Resource ◽  
Heuristic Algorithms ◽  
Optimization Problems ◽  
State Of The Art ◽  
Real Life ◽  
Distribution Networks ◽  
Water Resources Engineering

Abstract During the last three decades, the water resources engineering field has received a tremendous increase in the development and use of meta-heuristic algorithms like evolutionary algorithms (EA) and swarm intelligence (SI) algorithms for solving various kinds of optimization problems. The efficient design and operation of water resource systems is a challenging task and requires solutions through optimization. Further, real-life water resource management problems may involve several complexities like nonconvex, nonlinear and discontinuous functions, discrete variables, a large number of equality and inequality constraints, and often associated with multi-modal solutions. The objective function is not known analytically, and the conventional methods may face difficulties in finding optimal solutions. The issues lead to the development of various types of heuristic and meta-heuristic algorithms, which proved to be flexible and potential tools for solving several complex water resources problems. This paper provides a review of state-of-the-art methods and their use in planning and management of hydrological and water resources systems. It includes a brief overview of EAs (genetic algorithms, differential evolution, evolutionary strategies, etc.) and SI algorithms (particle swarm optimization, ant colony optimization, etc.), and applications in the areas of water distribution networks, water supply, and wastewater systems, reservoir operation and irrigation systems, watershed management, parameter estimation of hydrological models, urban drainage and sewer networks, and groundwater systems monitoring network design and groundwater remediation. This paper also provides insights, challenges, and need for algorithmic improvements and opportunities for future applications in the water resources field, in the face of rising problem complexities and uncertainties.

scholarly journals Investigation of Improved Cooperative Coevolution for Large-Scale Global Optimization Problems

Algorithms ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 146
Author(s):  
Aleksei Vakhnin ◽  
Evgenii Sopov
Keyword(s):  
Global Optimization ◽  
Evolutionary Algorithms ◽  
Numerical Experiments ◽  
Large Scale ◽  
Optimization Problems ◽  
State Of The Art ◽  
Fixed Number ◽  
High Dimensional ◽  
Cooperative Coevolution ◽  
Large Scale Problems

Modern real-valued optimization problems are complex and high-dimensional, and they are known as “large-scale global optimization (LSGO)” problems. Classic evolutionary algorithms (EAs) perform poorly on this class of problems because of the curse of dimensionality. Cooperative Coevolution (CC) is a high-performed framework for performing the decomposition of large-scale problems into smaller and easier subproblems by grouping objective variables. The efficiency of CC strongly depends on the size of groups and the grouping approach. In this study, an improved CC (iCC) approach for solving LSGO problems has been proposed and investigated. iCC changes the number of variables in subcomponents dynamically during the optimization process. The SHADE algorithm is used as a subcomponent optimizer. We have investigated the performance of iCC-SHADE and CC-SHADE on fifteen problems from the LSGO CEC’13 benchmark set provided by the IEEE Congress of Evolutionary Computation. The results of numerical experiments have shown that iCC-SHADE outperforms, on average, CC-SHADE with a fixed number of subcomponents. Also, we have compared iCC-SHADE with some state-of-the-art LSGO metaheuristics. The experimental results have shown that the proposed algorithm is competitive with other efficient metaheuristics.

Use of Multiobjective Evolutionary Algorithms in Water Resources Engineering

2010 ◽  
pp. 45-82 ◽  
Author(s):  
Francisco Venícius Fernandes Barros ◽  
Eduardo Sávio Passos Rodrigues Martins ◽  
Luiz Sérgio Vasconcelos Nascimento ◽  
Dirceu Silveira Reis
Keyword(s):  
Water Resources ◽  
Evolutionary Algorithms ◽  
Multiobjective Evolutionary Algorithms ◽  
Water Resources Engineering

scholarly journals Enriched Particle Swarm Optimization Created By Varying Parameters for Optimization

2019 ◽  
Vol 8 (3) ◽  
pp. 8259-8265
Keyword(s):  
Particle Swarm Optimization ◽  
Swarm Intelligence ◽  
Optimization Problems ◽  
Real Life ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Slow Convergence ◽  
Inertia Weight ◽  
Life Problems ◽  
Fixed Constant

Particle swarm optimization (PSO) is one of the most capable algorithms that reside to the swarm intelligence (SI) systems. Recently, it becomes very popular and renowned because of the easy implementation in complex/real life optimization problems. However, PSO has some observable drawbacks such as diversity maintenance, pre convergence and/or slow convergence speed. The ultimate success of PSO depends on the velocity update of the particles. Velocity has a significant dependence on its multiplied coefficient like inertia weight and acceleration factors. To increase the ability of PSO, this paper introduced an enriched PSO (namely ePSO), to solve hard optimization problems more precisely, efficiently and reliably. In ePSO novel gradually decreased inertia weight (as an alternative of a fixed constant value) and new gradually decreased and/or increased acceleration factors (meant for cognitive and social modules) is introduced. Proposed ePSO is used to solve four well known typical unconstrained benchmark functions and four complex unconstrained real life problems. The overall observation shows that proposed new algorithm ePSO is fitter than the compared algorithms significantly and statistically. Moreover, the convergence accuracy and speed of ePSO are also improved effectively

scholarly journals A Short Review on Computational Hydraulics in the context of Water Resources Engineering

2021 ◽  
Author(s):  
Shiblu Sarker
Keyword(s):  
Water Resources ◽  
Differential Equations ◽  
Numerical Technique ◽  
Real Life ◽  
Short Review ◽  
Sea Waves ◽  
Mathematical Equations ◽  
Computational Hydraulics ◽  
Water Resources Engineering ◽  
Volume Method

The term hydraulics concerned with the conveyance of water that can consist of very simple processes to complex physical processes, such as flow in open rivers, flow in pipes, flow of nutrients/sediments, flow of ground water to sea waves. The study of hydraulics is primarily a mixture of theory and experiments. Computational hydraulics is very helpful in-order to quantify and predict flow nature and behavior. Mathematical model is backbone of the computational hydraulics that consist simple to complex mathematical equations with linear and/or non-linear terms and ordinary or partial differential equations. Analytical solution of this mathematical equations is not feasible in the majority of cases. In this consequences, mathematical models are solved using different numerical techniques and associated schemes. In this manuscript we will review hydraulic principles along with their mathematical equations. Then we will learn some commonly used numerical technique to solve different types of differential equations related to the hydraulics. Among them the Finite Difference Method (FDM), Finite Element Method (FEM) and Finite Volume Method (FVM) will be discussed along with their use in real-life applications in the context of water resources engineering.

scholarly journals Proposition of New Metaphor-Less Algorithms for Reservoir Operation

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Vartika Paliwal ◽  
Aniruddha D. Ghare ◽  
Ashwini B. Mirajkar ◽  
Neeraj Dhanraj Bokde ◽  
Zaher Mundher Yaseen
Keyword(s):  
Water Resources ◽  
Water Resources Management ◽  
Reservoir Operation ◽  
Heuristic Algorithms ◽  
Optimization Problems ◽  
Computational Time ◽  
Benchmark Problems ◽  
Operation System ◽  
Resources Management ◽  
Operation Optimization

Based on the current water crisis scenario, effective water resources management can play an essential role. Reservoir operation optimization is part of water resources management. Reservoir operation optimization is difficult as it involves a large number of variables and constraints to achieve this goal. The present study aims at exploring the performance of recently developed heuristic algorithms—Rao algorithms as applied to the reservoir operation studies for the first time. Rao algorithms are metaphor-less algorithms that require only basic parameters—population size and function evaluations. In the present study, Rao algorithms have been applied to two case studies: discrete four-reservoir operation system problem and continuous four-reservoir operation system problem (benchmark problems) for the assessment of their performance vis-à-vis other algorithms from the literature. The results showed that the Rao-1 algorithm provided the optimal solution with the least function evaluations when compared to Rao-2, Rao-3, and other algorithms applied in the past to the same benchmark problem. Consequently, the Rao-1 model is found to be superior to these approaches by taking less computational time. Hence, the Rao-1 algorithm can be considered suitable for application to reservoir operation optimization problems.

scholarly journals Evolutionary Algorithms Applied to Antennas and Propagation: A Review of State of the Art

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Sotirios K. Goudos ◽  
Christos Kalialakis ◽  
Raj Mittra
Keyword(s):  
Evolutionary Algorithms ◽  
Optimization Problems ◽  
State Of The Art ◽  
Antenna Design ◽  
Design Problems ◽  
Swarm Optimization ◽  
Case Examples ◽  
Engineering Problems ◽  
Primary Focus ◽  
Nature Inspired Algorithms

A review of evolutionary algorithms (EAs) with applications to antenna and propagation problems is presented. EAs have emerged as viable candidates for global optimization problems and have been attracting the attention of the research community interested in solving real-world engineering problems, as evidenced by the fact that very large number of antenna design problems have been addressed in the literature in recent years by using EAs. In this paper, our primary focus is on Genetic Algorithms (GAs), Particle Swarm Optimization (PSO), and Differential Evolution (DE), though we also briefly review other recently introduced nature-inspired algorithms. An overview of case examples optimized by each family of algorithms is included in the paper.

scholarly journals Dragonfly Algorithm and Its Hybrids: A Survey on Performance, Objectives and Applications

Sensors ◽  
2021 ◽  
Vol 21 (22) ◽  
pp. 7542
Author(s):  
Bibi Aamirah Shafaa Emambocus ◽  
Muhammed Basheer Jasser ◽  
Aida Mustapha ◽  
Angela Amphawan
Keyword(s):  
Evolutionary Algorithms ◽  
Swarm Intelligence ◽  
Optimization Problems ◽  
Low Cost ◽  
Dragonfly Algorithm ◽  
Robust Solutions ◽  
Intelligence Algorithm ◽  
Comprehensive Survey ◽  
Swarm Intelligence Algorithm

Swarm intelligence is a discipline which makes use of a number of agents for solving optimization problems by producing low cost, fast and robust solutions. The dragonfly algorithm (DA), a recently proposed swarm intelligence algorithm, is inspired by the dynamic and static swarming behaviors of dragonflies, and it has been found to have a higher performance in comparison to other swarm intelligence and evolutionary algorithms in numerous applications. There are only a few surveys about the dragonfly algorithm, and we have found that they are limited in certain aspects. Hence, in this paper, we present a more comprehensive survey about DA, its applications in various domains, and its performance as compared to other swarm intelligence algorithms. We also analyze the hybrids of DA, the methods they employ to enhance the original DA, their performance as compared to the original DA, and their limitations. Moreover, we categorize the hybrids of DA according to the type of problem that they have been applied to, their objectives, and the methods that they utilize.

scholarly journals OVERVIEW OF HEURISTIC AND METAHEURISTIC ALGORITHMS

2020 ◽  
Vol 71 (3) ◽  
pp. 242-247
Author(s):  
D. Nurserik ◽  
◽  
F.R. Gusmanova ◽  
G.А. Abdulkarimova ◽  
K.S. Dalbekova ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Evolutionary Algorithms ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Heuristic Algorithms ◽  
Optimization Problems ◽  
Finite Automata ◽  
Routing Problem ◽  
General Terms ◽  
Suboptimal Solution

The article discusses the use of heuristic algorithms for optimization problems. The algorithms for stochastic optimization are described, which constitute the main properties of the metaheuristic and its classes. Evolutionary algorithms are described in general terms. In particular, the main steps and properties of genetic algorithms are presented. The main goal of this article is to solve the vehicle routing problem using a metaheuristic algorithm. The vehicle routing problem is a complex combinatorial NP-complete optimization problem. It is shown that the metaheuristic approach to solving the problem allows one to obtain a suboptimal solution without examining the entire space of possible solutions. The genetic algorithm belongs to the group of evolutionary algorithms. The definitions are briefly given to the terms characteristic of the genetic algorithm: gene, chromosome, personality (descendant), population, descendant operators, crossing, mutation, crossover. Application of the theory of finite automata in a genetic algorithm is described. The terminology and scheme of the genetic algorithm for solving various problems are proposed.

scholarly journals An MOEA/D-ACO with PBI for Many-Objective Optimization

2018 ◽  
Vol 2018 ◽  
pp. 1-9
Author(s):  
Tianbai Ling ◽  
Chen Wang
Keyword(s):  
Evolutionary Algorithms ◽  
Multiobjective Optimization ◽  
Selection Pressure ◽  
Optimization Problems ◽  
State Of The Art ◽  
Experimental Results ◽  
Significant Problem ◽  
Adjustment Method ◽  
Multiobjective Optimization Problems ◽  
Single Objective

Evolutionary algorithms (EAs) are an important instrument for solving the multiobjective optimization problems (MOPs). It has been observed that the combined ant colony (MOEA/D-ACO) based on decomposition is very promising for MOPs. However, as the number of optimization objectives increases, the selection pressure will be released, leading to a significant reduction in the performance of the algorithm. It is a significant problem and challenge in the MOEA/D-ACO to maintain the balance between convergence and diversity in many-objective optimization problems (MaOPs). In the proposed algorithm, an MOEA/D-ACO with the penalty based boundary intersection distance (PBI) method (MOEA/D-ACO-PBI) is intended to solve the MaOPs. PBI decomposes the problems with many single-objective problems, a weighted vector adjustment method based on clustering, and uses different pheromone matrices to solve different single objectives proposed. Then the solutions are constructed and pheromone was updated. Experimental results on both CF1-CF4 and suits of C-DTLZ benchmarks problems demonstrate the superiority of the proposed algorithm in comparison with three state-of-the-art algorithms in terms of both convergence and diversity.

scholarly journals A state-of-the-Art review of heuristic and metaheuristic optimization techniques for the management of water resources

2022 ◽  
Author(s):  
Vijendra Kumar ◽  
S. M. Yadav
Keyword(s):  
Resource Management ◽  
Water Resource ◽  
Water Resource Management ◽  
State Of The Art ◽  
Real Life ◽  
Engineering Problem ◽  
Optimization Techniques ◽  
Comprehensive Overview ◽  
Metaheuristic Optimization ◽  
Domestic Use

Abstract Water resource management is a complex engineering problem, due to the stochastic nature of inflow, various demands and environmental flow downstream. With the increase in water consumption for domestic use and irrigation, it becomes more challenging. Many more difficulties, such as non-convex, nonlinear, multi-objective, and discontinuous functions, exist in real-life. From the past two decades, heuristic and metaheuristic optimization techniques have played a significant role in managing and providing better performance solutions. The popularity of heuristic and metaheuristic optimization techniques has increased among researchers due to their numerous benefits and possibilities. Researchers are attempting to develop more accurate and efficient models by incorporating novel methods and hybridizing existing ones. This paper's main contribution is to show the state-of-the-art of heuristic and metaheuristic optimization techniques in water resource management. The research provides a comprehensive overview of the various techniques within the context of a thorough evaluation and discussion. As a result, for water resource management problems, this study introduces the most promising evolutionary and swarm intelligence techniques. Hybridization, modifications, and algorithm variants are reported to be the most successful for improving optimization techniques. This survey can be used to aid hydrologists and scientists in deciding the proper optimization techniques.

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