A Novel Hybrid Evolutionary Data-Intelligence Algorithm for Irrigation and Power Production Management: Application to Multi-Purpose Reservoir Systems

Multi-purpose advanced systems are considered a complex problem in water resource management, and the use of data-intelligence methodologies in operating such systems provides major advantages for decision-makers. The current research is devoted to the implementation of hybrid novel meta-heuristic algorithms (e.g., the bat algorithm (BA) and particle swarm optimization (PSO) algorithm) to formulate multi-purpose systems for power production and irrigation supply. The proposed hybrid modelling method was applied for the multi-purpose reservoir system of Bhadra Dam, which is located in the state of Karnataka, India. The average monthly demand for irrigation is 142.14 (106 m3), and the amount of released water based on the new hybrid algorithm (NHA) is 141.25 (106 m3). Compared with the shark algorithm (SA), BA, weed algorithm (WA), PSO algorithm, and genetic algorithm (GA), the NHA decreased the computation time by 28%, 36%, 39%, 82%, and 88%, respectively, which represents an excellent enhancement result. The amount of released water based on the proposed hybrid method attains a more reliable index for the volumetric percentage and provides a more effective operation rule for supplying the irrigation demand. Additionally, the average demand for power production is 18.90 (106 kwh), whereas the NHA produces 18.09 (106 kwh) of power. Power production utilizing the NHA’s operation rule achieved a sufficient magnitude relative to that of stand-alone models, such as the BA, PSO, WA, SA, and GA. The excellent proficiency of the developed intelligence expert system is the result of the hybrid structure of the BA and PSO algorithm and the substitution of weaker solutions in each algorithm with better solutions from other algorithms. The main advantage of the proposed NHA is its ability to increase the diversity of solutions and hence avoid the worst possible solutions obtained using BA, that is, preventing a decrease in local optima. In addition, the NHA enhances the convergence rate obtained using the PSO algorithm. Hence, the proposed NHA as an intelligence model could contribute to providing reliable solutions for complex multi-purpose reservoir systems to optimize the operation rule for similar reservoir systems worldwide.

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Delayed improvement local search

Journal of Heuristics ◽

10.1007/s10732-021-09479-9 ◽

2021 ◽

Author(s):

Heber F. Amaral ◽

Sebastián Urrutia ◽

Lars M. Hvattum

Keyword(s):

Local Search ◽

Heuristic Algorithms ◽

Travelling Salesman Problem ◽

Computation Time ◽

The Novel ◽

Local Optima ◽

New Strategy ◽

Max Cut Problem ◽

Novel Method ◽

Actor-critic learning-based energy optimization for UAV access and backhaul networks

EURASIP Journal on Wireless Communications and Networking ◽

10.1186/s13638-021-01960-0 ◽

2021 ◽

Vol 2021 (1) ◽

Author(s):

Yaxiong Yuan ◽

Lei Lei ◽

Thang X. Vu ◽

Symeon Chatzinotas ◽

Sumei Sun ◽

...

Keyword(s):

Power Allocation ◽

Heuristic Algorithms ◽

Piecewise Linear ◽

Access Network ◽

Computation Time ◽

Combinatorial Problem ◽

Base Station ◽

Piecewise Linear Approximation ◽

Group Scheduling ◽

User Group

AbstractIn unmanned aerial vehicle (UAV)-assisted networks, UAV acts as an aerial base station which acquires the requested data via backhaul link and then serves ground users (GUs) through an access network. In this paper, we investigate an energy minimization problem with a limited power supply for both backhaul and access links. The difficulties for solving such a non-convex and combinatorial problem lie at the high computational complexity/time. In solution development, we consider the approaches from both actor-critic deep reinforcement learning (AC-DRL) and optimization perspectives. First, two offline non-learning algorithms, i.e., an optimal and a heuristic algorithms, based on piecewise linear approximation and relaxation are developed as benchmarks. Second, toward real-time decision-making, we improve the conventional AC-DRL and propose two learning schemes: AC-based user group scheduling and backhaul power allocation (ACGP), and joint AC-based user group scheduling and optimization-based backhaul power allocation (ACGOP). Numerical results show that the computation time of both ACGP and ACGOP is reduced tenfold to hundredfold compared to the offline approaches, and ACGOP is better than ACGP in energy savings. The results also verify the superiority of proposed learning solutions in terms of guaranteeing the feasibility and minimizing the system energy compared to the conventional AC-DRL.

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Performance Evaluation of Meta-Heuristic Algorithms for Task Scheduling in Cloud Environment

International Journal of Advanced Research in Science, Communication and Technology ◽

10.48175/ijarsct-1214 ◽

2021 ◽

pp. 87-93

Author(s):

Jai Bhagwan ◽

Sanjeev Kumar

Keyword(s):

Task Scheduling ◽

Heuristic Algorithms ◽

Minimum Cost ◽

Pso Algorithm ◽

Cloud Services ◽

Positive Role ◽

Current Time ◽

Heuristic Technique ◽

Software Services ◽

Cost Factors

Cloud Computing is one of the important fields in the current time of technological era. Here, the resources are available virtually for users according to pay-per-usage. Many industries are providing cloud services nowadays as pay for usage which reduces the computing cost drastically. The updated software services, hardware services can be provided to the user at a minimum cost. The target of the industries and scientists is to reduce the computing cost by various technologies. Resource management or task scheduling may also play a positive role in this regard. There are various virtual machine management algorithms available that can be tested and enhanced for research and benefit of the society. In this paper, three famous Max-Min, Ant Colony Optimization, and Particle Swarm Optimization algorithms have been used for experiments. After simulation results, it is found that the PSO algorithm is performing well for makes pan and cost factors. Further, a new algorithm can be proposed or a meta-heuristic technique can be enhanced or modified for getting better results.

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Optimal design of aerospace structures using recent meta-heuristic algorithms

Materials Testing ◽

10.1515/mt-2021-0024 ◽

2021 ◽

Vol 63 (11) ◽

pp. 1025-1031

Author(s):

Faik Fatih Korkmaz ◽

Mert Subran ◽

Ali Rıza Yıldız

Keyword(s):

Heuristic Algorithms ◽

Optimal Solution ◽

Von Mises Stress ◽

Grey Wolf Optimizer ◽

Local Optima ◽

Design And Optimization ◽

Shape Perturbation ◽

Novel Algorithms ◽

Von Mises ◽

Conventional Optimization

Abstract Most conventional optimization approaches are deterministic and based on the derivative information of a problem’s function. On the other hand, nature-inspired and evolution-based algorithms have a stochastic method for finding the optimal solution. They have become a more popular design and optimization tool, with a continually growing development of novel algorithms and new applications. Flexibility, easy implementation, and the capability to avoid local optima are significant advantages of these algorithms. In this study, shapes, and shape perturbation limits of a bracket part, which is used in aviation, have been set using the hypermorph tool. The objective function of the optimization problem is minimizing the volume, and the constraint is maximum von Mises stress on the structure. The grey wolf optimizer (GWO) and the moth-flame Optimizer (MFO) have been selected as nature-inspired evolution-based optimizers.

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Meta-Heuristic Parameter Optimization for ANN and Real-Time Applications of ANN

10.4018/978-1-6684-2408-7.ch008 ◽

2022 ◽

pp. 166-201

Author(s):

Asha Gowda Karegowda ◽

Devika G.

Keyword(s):

Neural Networks ◽

Real Time ◽

Parameter Optimization ◽

Heuristic Algorithms ◽

Search Space ◽

Optimization Method ◽

Classification Problems ◽

Local Optima ◽

Consistent Solution ◽

Real Time Applications

Artificial neural networks (ANN) are often more suitable for classification problems. Even then, training of ANN is a surviving challenge task for large and high dimensional natured search space problems. These hitches are more for applications that involves process of fine tuning of ANN control parameters: weights and bias. There is no single search and optimization method that suits the weights and bias of ANN for all the problems. The traditional heuristic approach fails because of their poorer convergence speed and chances of ending up with local optima. In this connection, the meta-heuristic algorithms prove to provide consistent solution for optimizing ANN training parameters. This chapter will provide critics on both heuristics and meta-heuristic existing literature for training neural networks algorithms, applicability, and reliability on parameter optimization. In addition, the real-time applications of ANN will be presented. Finally, future directions to be explored in the field of ANN are presented which will of potential interest for upcoming researchers.

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Evaluation of Hunting-Based Optimizers for a Quadrotor Sliding Mode Flight Controller

Robotics ◽

10.3390/robotics9020022 ◽

2020 ◽

Vol 9 (2) ◽

pp. 22

Author(s):

Josenalde Oliveira ◽

Paulo Moura Oliveira ◽

José Boaventura-Cunha ◽

Tatiana Pinho

Keyword(s):

Control Systems ◽

Sliding Mode ◽

Tracking Error ◽

Pso Algorithm ◽

Optimization Techniques ◽

Linear Control Systems ◽

Classical Particle ◽

Grey Wolf Optimizer ◽

Local Optima ◽

Chattering Effect

The design of Multi-Input Multi-Output nonlinear control systems for a quadrotor can be a difficult task. Nature inspired optimization techniques can greatly improve the design of non-linear control systems. Two recently proposed hunting-based swarm intelligence inspired techniques are the Grey Wolf Optimizer (GWO) and the Ant Lion Optimizer (ALO). This paper proposes the use of both GWO and ALO techniques to design a Sliding Mode Control (SMC) flight system for tracking improvement of altitude and attitude in a quadrotor dynamic model. SMC is a nonlinear technique which requires that its strictly coupled parameters related to continuous and discontinuous components be correctly adjusted for proper operation. This requires minimizing the tracking error while keeping the chattering effect and control signal magnitude within suitable limits. The performance achieved with both GWO and ALO, considering realistic disturbed flight scenarios are presented and compared to the classical Particle Swarm Optimization (PSO) algorithm. Simulated results are presented showing that GWO and ALO outperformed PSO in terms of precise tracking, for ideal and disturbed conditions. It is shown that the higher stochastic nature of these hunting-based algorithms provided more confidence in local optima avoidance, suggesting feasibility of getting a more precise tracking for practical use.

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Self-adaptive improved PSO algorithm to solve optimal operation of cascade reservoir systems

IOP Conference Series Earth and Environmental Science ◽

10.1088/1755-1315/344/1/012110 ◽

2019 ◽

Vol 344 ◽

pp. 012110

Author(s):

Y Q Xiang ◽

L Mo

Keyword(s):

Pso Algorithm ◽

Optimal Operation ◽

Reservoir Systems ◽

Self Adaptive ◽

Improved Pso

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A Self-Adaptive Discrete PSO Algorithm with Heterogeneous Parameter Values for Dynamic TSP

Entropy ◽

10.3390/e21080738 ◽

2019 ◽

Vol 21 (8) ◽

pp. 738 ◽

Cited By ~ 6

Author(s):

Łukasz Strąk ◽

Rafał Skinderowicz ◽

Urszula Boryczka ◽

Arkadiusz Nowakowski

Keyword(s):

Computation Time ◽

Pso Algorithm ◽

Discrete Particle Swarm Optimization ◽

Swarm Optimization ◽

Discrete Particle ◽

Discrete Pso ◽

Parameter Values ◽

Positive Effect ◽

Uniform Parameter

This paper presents a discrete particle swarm optimization (DPSO) algorithm with heterogeneous (non-uniform) parameter values for solving the dynamic traveling salesman problem (DTSP). The DTSP can be modeled as a sequence of static sub-problems, each of which is an instance of the TSP. In the proposed DPSO algorithm, the information gathered while solving a sub-problem is retained in the form of a pheromone matrix and used by the algorithm while solving the next sub-problem. We present a method for automatically setting the values of the key DPSO parameters (except for the parameters directly related to the computation time and size of a problem).We show that the diversity of parameters values has a positive effect on the quality of the generated results. Furthermore, the population in the proposed algorithm has a higher level of entropy. We compare the performance of the proposed heterogeneous DPSO with two ant colony optimization (ACO) algorithms. The proposed algorithm outperforms the base DPSO and is competitive with the ACO.

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An efficient algorithm for function optimization: modified stem cells algorithm

Open Engineering ◽

10.2478/s13531-012-0047-8 ◽

2013 ◽

Vol 3 (1) ◽

Cited By ~ 13

Author(s):

Mohammad Taherdangkoo ◽

Mahsa Paziresh ◽

Mehran Yazdi ◽

Mohammad Bagheri

Keyword(s):

Stem Cells ◽

Optimization Algorithm ◽

Optimization Algorithms ◽

Pso Algorithm ◽

Function Optimization ◽

Natural Behavior ◽

Local Optima ◽

Bee Colony ◽

Intelligent Behavior ◽

Improved Performance

AbstractIn this paper, we propose an optimization algorithm based on the intelligent behavior of stem cell swarms in reproduction and self-organization. Optimization algorithms, such as the Genetic Algorithm (GA), Particle Swarm Optimization (PSO) algorithm, Ant Colony Optimization (ACO) algorithm and Artificial Bee Colony (ABC) algorithm, can give solutions to linear and non-linear problems near to the optimum for many applications; however, in some case, they can suffer from becoming trapped in local optima. The Stem Cells Algorithm (SCA) is an optimization algorithm inspired by the natural behavior of stem cells in evolving themselves into new and improved cells. The SCA avoids the local optima problem successfully. In this paper, we have made small changes in the implementation of this algorithm to obtain improved performance over previous versions. Using a series of benchmark functions, we assess the performance of the proposed algorithm and compare it with that of the other aforementioned optimization algorithms. The obtained results prove the superiority of the Modified Stem Cells Algorithm (MSCA).

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Development of a Common Platform for Testing Metamodel Based Design Optimization Methods

Volume 3B: 39th Design Automation Conference ◽

10.1115/detc2013-12664 ◽

2013 ◽

Author(s):

Adel A. Younis ◽

George H. Cheng ◽

G. Gary Wang ◽

Zuomin Dong

Keyword(s):

Design Optimization ◽

Optimization Problems ◽

Optimization Algorithms ◽

Computation Time ◽

Optimization Methods ◽

Global Optimum ◽

Test Problems ◽

Test Bed ◽

Test Procedures ◽

Local Optima

Metamodel based design optimization (MBDO) algorithms have attracted considerable interests in recent years due to their special capability in dealing with complex optimization problems with computationally expensive objective and constraint functions and local optima. Conventional unimodal-based optimization algorithms and stochastic global optimization algorithms either miss the global optimum frequently or require unacceptable computation time. In this work, a generic testbed/platform for evaluating various MBDO algorithms has been introduced. The purpose of the platform is to facilitate quantitative comparison of different MBDO algorithms using standard test problems, test procedures, and test outputs, as well as to improve the efficiency of new algorithm testing and improvement. The platform consists of a comprehensive test function database that contains about 100 benchmark functions and engineering problems. The testbed accepts any optimization algorithm to be tested, and only requires minor modifications to meet the test-bed requirements. The testbed is useful in comparing the performance of competing algorithms through execution of same problems. It allows researchers and practitioners to test and choose the most suitable optimization tool for their specific needs. It also helps to increase confidence and reliability of the newly developed MBDO tools. Many new MBDO algorithms, including Mode Pursuing Sampling (MPS), Pareto Set Pursuing (PSP), and Space Exploration and Unimodal Region Elimination (SEUMRE), were tested in this work to demonstrate its functionality and benefits.

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