scholarly journals A hybrid SP-QPSO algorithm with parameter free adaptive penalty method for constrained global optimization problems

2018 ◽  
Vol 1 (1) ◽  
pp. 15-26
Author(s):  
D B Fatemeh ◽  
C K Loo ◽  
G Kanagaraj ◽  
S G Ponnambalam
Keyword(s):  
Penalty Method ◽  
Optimization Problems ◽  
Real Life ◽  
Search Space ◽  
Computational Time ◽  
Shuffled Complex Evolution ◽  
Centroidal Voronoi Tessellations ◽  
Adaptive Penalty ◽  
Adaptive Penalty Method

Most real-life optimization problems involve constraints which require a specialized mechanism to deal with them. The presence of constraints imposes additional challenges to the researchers motivated towards the development of new algorithm with efficient constraint handling mechanism. This paper attempts the suitability of newly developed hybrid algorithm, Shuffled Complex Evolution with Quantum Particle Swarm Optimization abbreviated as SP-QPSO, extended specifically designed for solving constrained optimization problems. The incorporation of adaptive penalty method guides the solutions to the feasible regions of the search space by computing the violation of each one. Further, the algorithm’s performance is improved by Centroidal Voronoi Tessellations method of point initialization promise to visit the entire search space. The effectiveness and the performance of SP-QPSO are examined by solving a broad set of ten benchmark functions and four engineering case study problems taken from the literature. The experimental results show that the hybrid version of SP-QPSO algorithm is not only overcome the shortcomings of the original algorithms but also outperformed most state-of-the-art algorithms, in terms of searching efficiency and computational time.

scholarly journals Stochastic Travelling Advisor Problem Simulation with a Case Study: A Novel Binary Gaining-Sharing Knowledge-Based Optimization Algorithm

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Said Ali Hassan ◽  
Yousra Mohamed Ayman ◽  
Khalid Alnowibet ◽  
Prachi Agrawal ◽  
Ali Wagdy Mohamed
Keyword(s):  
Optimization Algorithm ◽  
Optimization Problems ◽  
Real Life ◽  
Health And Safety ◽  
Working Hours ◽  
Search Space ◽  
Advisory Group ◽  
Deterministic Models ◽  
Knowledge Based

This article proposes a new problem which is called the Stochastic Travelling Advisor Problem (STAP) in network optimization, and it is defined for an advisory group who wants to choose a subset of candidate workplaces comprising the most profitable route within the time limit of day working hours. A nonlinear binary mathematical model is formulated and a real application case study in the occupational health and safety field is presented. The problem has a stochastic nature in travelling and advising times since the deterministic models are not appropriate for such real-life problems. The STAP is handled by proposing suitable probability distributions for the time parameters and simulating the problem under such conditions. Many application problems like this one are formulated as nonlinear binary programming models which are hard to be solved using exact algorithms especially in large dimensions. A novel binary version of the recently developed gaining-sharing knowledge-based optimization algorithm (GSK) to solve binary optimization problems is given. GSK algorithm is based on the concept of how humans acquire and share knowledge during their life span. The binary version of GSK (BGSK) depends mainly on two stages that enable BGSK for exploring and exploitation of the search space efficiently and effectively to solve problems in binary space. The generated simulation runs of the example are solved using the BGSK, and the output histograms and the best-fitted distributions for the total profit and for the route length are obtained.

DIFFERENT APPROACHES IN MULTIPLE-CRITERIA OPTIMIZATION USING THE TAGUCHI METHOD: A CASE STUDY IN A COLD HEADING PROCESS

2004 ◽  
Vol 03 (01) ◽  
pp. 53-68 ◽  
Author(s):  
A. S. MILANI ◽  
C. EL-LAHHAM ◽  
J. A. NEMES
Keyword(s):  
Taguchi Method ◽  
Optimization Problems ◽  
Real Life ◽  
Multiple Attribute Decision Making ◽  
Multiple Criteria ◽  
Multiple Criteria Optimization ◽  
Multiple Attribute ◽  
Overall Evaluation ◽  
Decision Making Model

Real life engineering problems usually require the satisfaction of different, potentially conflicting criteria. Design optimization, on the other hand, based on the conventional Taguchi method cannot accommodate more than one response. However, by the use of the overall evaluation criterion approach, the method can be applied to multiple-criteria optimization problems. This paper presents the use of different utility function methods as well as a multiple attribute decision-making model in the multiple-criteria optimization of a cold heading process. Different aspects of each method are discussed and compared.

Differential evolution with the adaptive penalty method for structural multi-objective optimization

2018 ◽  
Vol 20 (1) ◽  
pp. 65-88 ◽  
Author(s):  
Dênis E. C. Vargas ◽  
Afonso C. C. Lemonge ◽  
Helio J. C. Barbosa ◽  
Heder S. Bernardino
Keyword(s):  
Differential Evolution ◽  
Penalty Method ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Adaptive Penalty ◽  
Adaptive Penalty Method

A New Adaptive Penalty Method for Constrained Genetic Algorithm and Its Application to Water Distribution Systems

2006 ◽  
Author(s):  
Berge Djebedjian ◽  
Ashraf Yaseen ◽  
Magdy Abou Rayan
Keyword(s):  
Genetic Algorithm ◽  
Penalty Function ◽  
Penalty Method ◽  
Distribution Systems ◽  
Water Distribution ◽  
Optimization Problem ◽  
Water Distribution Systems ◽  
Building Blocks ◽  
Adaptive Penalty ◽  
Adaptive Penalty Method

This paper presents a new adaptive penalty method for genetic algorithms (GA). External penalty functions have been used to convert a constrained optimization problem into an unconstrained problem for GA-based optimization. The success of the genetic algorithm application to the design of water distribution systems depends on the choice of the penalty function. The optimal design of water distribution systems is a constrained non-linear optimization problem. Constraints (for example, the minimum pressure requirements at the nodes) are generally handled within genetic algorithm optimization by introducing a penalty cost function. The optimal solution is found when the pressures at some nodes are close to the minimum required pressure. The goal of an adaptive penalty function is to change the value of the penalty draw-down coefficient during the search allowing exploration of infeasible regions to find optimal building blocks, while preserving the feasibility of the final solution. In this study, a new penalty coefficient strategy is assumed to increase with the total cost at each generation and inversely with the total number of nodes. The application of the computer program to case studies shows that it finds the least cost in a favorable number of function evaluations if not less than that in previous studies and it is computationally much faster when compared with other studies.

scholarly journals Reducing the Computational Time for the Kemeny Method by Exploiting Condorcet Properties

Mathematics ◽  
2021 ◽  
Vol 9 (12) ◽  
pp. 1380
Author(s):  
Noelia Rico ◽  
Camino R. Vela ◽  
Raúl Pérez-Fernández ◽  
Irene Díaz
Keyword(s):  
Choice Theory ◽  
State Of The Art ◽  
Social Choice Theory ◽  
Real Life ◽  
Search Space ◽  
Exact Algorithms ◽  
Computational Time ◽  
Preference Aggregation ◽  
Life Problems ◽  
Ranking Aggregation

Preference aggregation and in particular ranking aggregation are mainly studied by the field of social choice theory but extensively applied in a variety of contexts. Among the most prominent methods for ranking aggregation, the Kemeny method has been proved to be the only one that satisfies some desirable properties such as neutrality, consistency and the Condorcet condition at the same time. Unfortunately, the problem of finding a Kemeny ranking is NP-hard, which prevents practitioners from using it in real-life problems. The state of the art of exact algorithms for the computation of the Kemeny ranking experienced a major boost last year with the presentation of an algorithm that provides searching time guarantee up to 13 alternatives. In this work, we propose an enhanced version of this algorithm based on pruning the search space when some Condorcet properties hold. This enhanced version greatly improves the performance in terms of runtime consumption.

scholarly journals Modified Grasshopper Optimization Algorithm Based Genetic Algorithm for Global Optimization Problems: The System of Nonlinear Equations Case Study

2021 ◽  
Author(s):  
Hala A. Omar ◽  
Mohammed El-Shorbagy
Keyword(s):  
Genetic Algorithm ◽  
Optimization Algorithm ◽  
Local Minimum ◽  
Optimization Problems ◽  
Search Space ◽  
System Of Nonlinear Equations ◽  
Grasshopper Optimization Algorithm ◽  
Speed Up ◽  
Grasshopper Optimization

Abstract Grasshopper optimization algorithm (GOA) is one of the promising optimization algorithms for optimization problems. But, it has the main drawback of trapping into a local minimum, which causes slow convergence or inability to detect a solution. Several modifications and combinations have been proposed to overcome this problem. In this paper, a modified grasshopper optimization algorithm (MGOA) based genetic algorithm (GA) is proposed to overcome this problem. Modifications rely on certain mathematical assumptions and varying the domain of the Cmax control parameter to escape from the local minimum and move the search process to a new improved point. Parameter C is one of the most important parameters in GOA where it balances the exploration and exploitation of the search space. These modifications aim to lead to speed up the convergence rate by reducing the repeated solutions and the number of iterations. The proposed algorithm will be tested on the 19 main test functions to verify and investigate the influence of the proposed modifications. In addition, the algorithm will be applied to solve 5 different cases of nonlinear systems with different types of dimensions and regularity to show the reliability and efficiency of the proposed algorithm. Good results were achieved compared to the original GOA.

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