Evaluation of EHO Algorithm’s in solving various real-life problems

2020 ◽  
Vol 1 (2) ◽  
pp. 46-60
Author(s):  
Ahmed R. Ridwan
Keyword(s):  
Pid Controller ◽  
Search Algorithm ◽  
Real Life ◽  
Service Selection ◽  
Benchmark Problems ◽  
Herding Behavior ◽  
Life Problems ◽  
Metaheuristic Search ◽  
Pid Controller Tuning ◽  
Hard Problems

Abstract:  This paper, presents a new swarm-based metaheuristic search algorithm, known as Elephant Herding Optimization (EHO), which is proposed for solving various daily real-life problems such as benchmark problems, Service Selection in QoS-Aware Web Service Composition, Energy-Based Localization, PID controller tuning, Appliance Scheduling in Smart Grid identification and other problems. The EHO method is inspired by the herding behavior of elephant group. In nature, elephants live in clans under the leadership of a matriarch (female elephant), while the male elephants separate from their family when they grow up. These two behaviors are used by EHO as two operators: clan updating operator and separating operator, which will be used in the optimizing process. Elephant Herding Optimization (EHO) is used to solve NP-Hard problems.

Gravitational Search Algorithm Optimization for PID Controller Tuning in Waste-water Treatment Process

2015 ◽  
Vol 73 (3) ◽  
Author(s):  
Mohamad Saiful Islam Aziz ◽  
Sophan Wahyudi Nawawi ◽  
Shahdan Sudin ◽  
Norhaliza Abdul Wahab ◽  
Mahdi Faramarzi ◽  
...  
Keyword(s):  
Pid Controller ◽  
Treatment Process ◽  
Search Algorithm ◽  
Waste Water Treatment ◽  
Gravitational Search Algorithm ◽  
Water Treatment Process ◽  
Pid Controller Tuning ◽  
Gravitational Search ◽  
Law Of Gravity

This paper presents a new approach of optimization technique in the controller parameter tuning for waste-water treatment process (WWTP) application. In the case study of WWTP, PID controller is used to control substrate (S) and dissolved oxygen (DO) concentration level. Too many parameters that need to be controlled make the system becomes complicated. Gravitational Search Algorithm (GSA) is used as the main method for PID controller tuning process. GSA is based on Newton's Law of Gravity and mass interaction. In this algorithm, the searcher agents survey the masses that interact with each other using law of gravity and law of motion. For WWTP system, the activated sludge reactor is used and this system is multi-input multi-output (MIMO) process. MATLAB is used as the platform to perform the simulation, where this optimization is compared to other established optimization method such as the Particle Swarm Optimization (PSO) to determine whether GSA has better features compared to PSO or vice-versa. Based on this case-study, the results show that transient response of GSA-PID was 20%-30% better compared to transient response of the PSO-PID controller.

scholarly journals Robustness and Stability in Constraint Programming under Dynamism and Uncertainty

2014 ◽  
Vol 49 ◽  
pp. 49-78 ◽  
Author(s):  
L. Climent ◽  
R. J. Wallace ◽  
M. A. Salido ◽  
F. Barber
Keyword(s):  
Constraint Programming ◽  
Original Problem ◽  
Search Algorithm ◽  
Real Life ◽  
Dynamic Environment ◽  
Dynamic Environments ◽  
General Purpose ◽  
Constraint Satisfaction Problems ◽  
Life Problems ◽  
Random Instances

Many real life problems that can be solved by constraint programming, come from uncertain and dynamic environments. Because of the dynamism, the original problem may change over time, and thus the solution found for the original problem may become invalid. For this reason, dealing with such problems has become an important issue in the fields of constraint programming. In some cases, there exist extant knowledge about the uncertain and dynamic environment. In other cases, this information is fragmentary or unknown. In this paper, we extend the concept of robustness and stability for Constraint Satisfaction Problems (CSPs) with ordered domains, where only limited assumptions need to be made as to possible changes. We present a search algorithm that searches for both robust and stable solutions for CSPs of this nature. It is well-known that meeting both criteria simultaneously is a desirable objective for constraint solving in uncertain and dynamic environments. We also present compelling evidence that our search algorithm outperforms other general-purpose algorithms for dynamic CSPs using random instances and benchmarks derived from real life problems.

scholarly journals Optimization of PID Controller to Stabilize Quadcopter Movements Using Meta-Heuristic Search Algorithms

2021 ◽  
Vol 11 (14) ◽  
pp. 6492
Author(s):  
Alaa Sheta ◽  
Malik Braik ◽  
Dheeraj Reddy Maddi ◽  
Ahmed Mahdy ◽  
Sultan Aljahdali ◽  
...  
Keyword(s):  
Heuristic Search ◽  
Pid Controller ◽  
Heuristic Algorithms ◽  
Search Algorithm ◽  
Fitness Function ◽  
Movement Control ◽  
External Perturbations ◽  
Heuristic Search Algorithms ◽  
Significant Damage ◽  
Pid Controller Tuning

Quadrotor UAVs are one of the most preferred types of small unmanned aerial vehicles, due to their modest mechanical structure and propulsion precept. However, the complex non-linear dynamic behavior of the Proportional Integral Derivative (PID) controller in these vehicles requires advanced stabilizing control of their movement. Additionally, locating the appropriate gain for a model-based controller is relatively complex and demands a significant amount of time, as it relies on external perturbations and the dynamic modeling of plants. Therefore, developing a method for the tuning of quadcopter PID parameters may save effort and time, and better control performance can be realized. Traditional methods, such as Ziegler–Nichols (ZN), for tuning quadcopter PID do not provide optimal control and might leave the system with potential instability and cause significant damage. One possible approach that alleviates the tough task of nonlinear control design is the use of meta-heuristics that permit appropriate control actions. This study presents PID controller tuning using meta-heuristic algorithms, such as Genetic Algorithms (GAs), the Crow Search Algorithm (CSA) and Particle Swarm Optimization (PSO) to stabilize quadcopter movements. These meta-heuristics were used to control the position and orientation of a PID controller based on a fitness function proposed to reduce overshooting by predicting future paths. The obtained results confirmed the efficacy of the proposed controller in felicitously and reliably controlling the flight of a quadcopter based on GA, CSA and PSO. Finally, the simulation results related to quadcopter movement control using PSO presented impressive control results, compared to GA and CSA.

A novel hybrid water wave optimization algorithm for solving complex constrained engineering problems

2021 ◽  
Vol 63 (6) ◽  
pp. 560-564
Author(s):  
Dildar Gürses ◽  
Nantiwat Pholdee ◽  
Sujin Bureerat ◽  
Sadiq M. Sait ◽  
Ali Rıza Yıldız
Keyword(s):  
Optimization Algorithm ◽  
Real World ◽  
Water Wave ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Real Life ◽  
Processing Parameters ◽  
Parameters Optimization ◽  
Optimization Approach ◽  
Life Problems

Abstract In this work, a new hybrid optimization algorithm (HWW-NM), which combines the Nelder-Mead local search algorithm with the water wave algorithm, is introduced to solve real-world engineering optimization problems. This paper is one of the first studies in which both the water wave algorithm and the HWW-NM are applied to processing parameters optimization for manufacturing processes. HWW-NM performance is measured using the cantilever beam problem. Additionally, a problem for milling manufacturing optimization is posed and solved to evaluate HWW-NM performance in real-world applications. The results reveal that HWW-NM is an attractive optimization approach for optimizing real-life problems.

Bio-Inspired Metaheuristics

2020 ◽  
Vol 10 (4) ◽  
pp. 1-18
Author(s):  
Rachid Kaleche ◽  
Zakaria Bendaoud ◽  
Karim Bouamrane
Keyword(s):  
Real Life ◽  
Optimal Solution ◽  
Np Hard ◽  
Exact Methods ◽  
Approximate Methods ◽  
Life Problems ◽  
Comprehensive Survey ◽  
Hard Problems ◽  
Exact And Approximate Methods ◽  
Np Hard Problems

In real life, problems becoming more complicated, among them NP-Hard problems. To resolve them, two families of methods exist, exact and approximate methods. When exact methods provide the optimal solution in an unacceptable amount of time, the approximate ones provide good solutions in a reasonable amount of time. Approximate methods are two kinds, heuristics and metaheuristics. The first ones are problem specific, while metaheuristics are independent from problems. A broad number of metaheuristics are inspired from nature, specially from biology. These bio-inspired metaheuristics are easy to implement and provide interesting results. This paper aims to provide a comprehensive survey of bio-inspired metaheuristics, their classification, principals, algorithms, their application domains, and a comparison between them.

Comparison of nonlinear solvers within continuation method for steady-state variably saturated groundwater flow modelling

2021 ◽  
Vol 36 (4) ◽  
pp. 183-195
Author(s):  
Denis Anuprienko
Keyword(s):  
Steady State ◽  
Line Search ◽  
Search Algorithm ◽  
Continuation Method ◽  
Real Life ◽  
Richards Equation ◽  
Life Problems ◽  
Nonlinear Solvers ◽  
Groundwater Flow Modelling ◽  
Search Approach

Abstract Nonlinearity continuation method, applied to boundary value problems for steady-state Richards equation, gradually approaches the solution through a series of intermediate problems. Originally, the Newton method with simple line search algorithm was used to solve the intermediate problems. In the present paper, other solvers such as Picard and mixed Picard–Newton methods are considered, combined with slightly modified line search approach. Numerical experiments are performed with advanced finite volume discretizations for model and real-life problems.

scholarly journals Single-based and Population-Based Metaheuristics Algorithms Performances in Solving NP-hard Problems

2021 ◽  
Author(s):  
Saman Almufti
Keyword(s):  
Real Life ◽  
Optimal Solution ◽  
Population Based ◽  
Performance Evaluations ◽  
Np Hard ◽  
Life Problems ◽  
Hard Problems ◽  
Optimum Solution ◽  
Over Time ◽  
Np Hard Problems

Metaheuristics is one of the most well-known field of researches uses to find optimum solution for Non-deterministic polynomial hard problems (NP-Hard), that are difficult to find an optimal solution in a polynomial time. Over time many algorithms have been developed based on the heuristics to solve difficult real-life problems, this paper will introduce Metaheuristic-based algorithms and its classifications, Non-deterministic polynomial hard problems. It also will compare the performance two metaheuristic-based algorithms (Elephant Herding optimization algorithm and Tabu Search) to solve Traveling Salesman Problem (TSP), which is one of the most known problem belongs to Non-deterministic polynomial hard problem and widely used in the performance evaluations for different metaheuristics-based optimization algorithms. the experimental results of the paper compare the results of EHO and TS for solving 10 different problems from the TSPLIB95.

The Psychologist in the Medical School: An Example of Solving Real-Life Problems

1970 ◽  
Author(s):  
Matisyohu Weisenberg ◽  
Carl Eisdorfer ◽  
C. Richard Fletcher ◽  
Murray Wexler
Keyword(s):  
Medical School ◽  
Real Life ◽  
Life Problems
Sign in / Sign up

Export Citation Format

Share Document