scholarly journals An integrated cuckoo search optimizer for single and multi-objective optimization problems

2021 ◽  
Vol 7 ◽  
pp. e370
Author(s):  
Xiangbo Qi ◽  
Zhonghu Yuan ◽  
Yan Song
Keyword(s):  
Optimization Problems ◽  
Cuckoo Search ◽  
Search Strategies ◽  
Comprehensive Analysis ◽  
Experimental Results ◽  
Test Problems ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Multiple Strategies ◽  
Single Objective

Integrating heterogeneous biological-inspired strategies and mechanisms into one algorithm can avoid the shortcomings of single algorithm. This article proposes an integrated cuckoo search optimizer (ICSO) for single objective optimization problems, which incorporates the multiple strategies into the cuckoo search (CS) algorithm. The paper also considers the proposal of multi-objective versions of ICSO called MOICSO. The two algorithms presented in this paper are benchmarked by a set of benchmark functions. The comprehensive analysis of the experimental results based on the considered test problems and comparisons with other recent methods illustrate the effectiveness of the proposed integrated mechanism of different search strategies and demonstrate the performance superiority of the proposed algorithm.

scholarly journals Multi-objective Genetic Algorithms: Problem Difficulties and Construction of Test Problems

1999 ◽  
Vol 7 (3) ◽  
pp. 205-230 ◽  
Author(s):  
Kalyanmoy Deb
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Optimization Problems ◽  
Test Problems ◽  
Pareto Optimal ◽  
Multi Objective Optimization ◽  
Pareto Optimal Front ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm ◽  
Single Objective

In this paper, we study the problem features that may cause a multi-objective genetic algorithm (GA) difficulty in converging to the true Pareto-optimal front. Identification of such features helps us develop difficult test problems for multi-objective optimization. Multi-objective test problems are constructed from single-objective optimization problems, thereby allowing known difficult features of single-objective problems (such as multi-modality, isolation, or deception) to be directly transferred to the corresponding multi-objective problem. In addition, test problems having features specific to multi-objective optimization are also constructed. More importantly, these difficult test problems will enable researchers to test their algorithms for specific aspects of multi-objective optimization.

An Interactive Neural Network for Constrained Multi-Objective Optimization with Application to the Design of Digital Filters

2012 ◽  
Vol 433-440 ◽  
pp. 2808-2816
Author(s):  
Jian Jin Zheng ◽  
You Shen Xia
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Computational Complexity ◽  
Digital Filters ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Optimal Solution ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Single Objective

This paper presents a new interactive neural network for solving constrained multi-objective optimization problems. The constrained multi-objective optimization problem is reformulated into two constrained single objective optimization problems and two neural networks are designed to obtain the optimal weight and the optimal solution of the two optimization problems respectively. The proposed algorithm has a low computational complexity and is easy to be implemented. Moreover, the proposed algorithm is well applied to the design of digital filters. Computed results illustrate the good performance of the proposed algorithm.

scholarly journals A NEW PROBABILISTIC ALGORITHM FOR SOLVING A CLASS OF SINGLE OR MULTI-OBJECTIVE OPTIMAL PROBLEMS

2009 ◽  
Vol 12 (11) ◽  
pp. 11-26
Author(s):  
Hao Van Tran ◽  
Thong Huu Nguyen
Keyword(s):  
Numerical Optimization ◽  
Optimization Problems ◽  
Optimization Technique ◽  
Fixed Number ◽  
Optimal Solutions ◽  
Multi Objective Optimization ◽  
Probabilistic Algorithm ◽  
Multi Objective ◽  
Single Objective

We consider a class of single-objective optimization problems which haves the character: there is a fixed number k (1≤k<n) that is independent of the size n of the problem such that if we only need to change values of k variables then it has the ability to find a better solution than the current one, let us call it Ok. In this paper, we propose a new numerical optimization technique, Search Via Probability (SVP) algorithm, for solving single objective optimization problems of the class Ok. The SVP algorithm uses probabilities to control the process of searching for optimal solutions. We calculate probabilities of the appearance of a better solution than the current one on each of iterations, and on the performance of SVP algorithm we create good conditions for its appearance. We tested this approach by implementing the SVP algorithm on some test single-objective and multi objective optimization problems, and we found good and very stable results.

scholarly journals An Overview of Few Nature Inspired Optimization Techniques and Its Reliability Applications

2020 ◽  
Vol 5 (4) ◽  
pp. 732-743
Author(s):  
Nitin Uniyal ◽  
Sangeeta Pant ◽  
Anuj Kumar
Keyword(s):  
Optimization Problems ◽  
Optimization Techniques ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Research Fields ◽  
Research Problems ◽  
Nature Inspired Algorithms ◽  
New Algorithms ◽  
Single Objective

Optimization has been a hot topic due to its inevitably in the development of new algorithms in almost every applied branch of Mathematics. Despite the broadness of optimization techniques in research fields, there is always an open scope of further refinement. We present here an overview of nature-inspired optimization with a subtle background of fundamentals and classification and their reliability applications. An attempt has been made to exhibit the contrast nature of multi objective optimization as compared to single objective optimization. Though there are various techniques to achieve the optimality in optimization problems but nature inspired algorithms have proved to be very efficient and gained special attention in modern research problems. The purpose of this article is to furnish the foundation of few nature inspired optimization techniques and their reliability applications to an interested researcher.

Multi-Objective Optimal Control Design With the Simple Cell Mapping Method

2013 ◽  
Author(s):  
Yousef Sardahi ◽  
Yousef Naranjani ◽  
Wei Liang ◽  
Jian-Qiao Sun ◽  
Carlos Hernandez ◽  
...  
Keyword(s):  
Optimal Control ◽  
Optimization Problems ◽  
Control Design ◽  
Optimal Solutions ◽  
Multi Objective Optimization ◽  
Cell Mapping ◽  
Multi Objective ◽  
Simple Cell Mapping ◽  
Pareto Sets ◽  
Single Objective

Controls are often designed to meet different and conflicting goals. Consider the well-known LQR optimal control. The performance index contains a response measure and a control penalty, which are conflicting requirements. Proper linear or nonlinear combinations of the conflicting objective functions have led to single objective optimization problems. However, such a single objective optimization is dependent on the combination algorithm, and only provides a narrow window of all possible optimal solutions that a system may have. Multi-objective optimization provides a set of optimal solutions, known as Pareto set. There have been many studies of search algorithms for Pareto sets of multi-objective optimization problems for complex dynamical systems. Recently, the simple cell mapping (SCM) method due to C.S. Hsu has been found to be a highly effective tool to compute Pareto sets. This paper applies the SCM method to several control design problems of linear and nonlinear dynamical systems. The results of the work are very exciting to report.

scholarly journals Sen's Multi Objective Optimization (MOO) Technique-An Extension

2020 ◽  
Vol 06 (09) ◽  
pp. 77-79
Author(s):  
Chandra Sen
Keyword(s):  
Linear Programming ◽  
Optimization Problems ◽  
Multiple Objectives ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Single Objective

Linear programming has been very popular for achieving (maximizing or minimizing) a single objective with certain constraints. However, when objectives are more than one, linear programming becomes inefficient. Sen's multi-objective optimization (MOO) technique [1] is efficient in achieving multiple objectives simultaneously. Few modifications in Sen's MOO technique are proposed for improving its applicability for solving multi-objective optimization problems.

MOTEO: A Novel Multi-Objective Thermal Exchange Optimization Algorithm for Optimal Design of Truss Structures

2021 ◽  
Author(s):  
Nima Khodadadi ◽  
Siamak Talatahari ◽  
Armin Dadras Eslamlou
Keyword(s):  
Structural Design ◽  
Optimization Problems ◽  
Truss Structures ◽  
Metaheuristic Algorithm ◽  
Multi Objective Optimization ◽  
Thermal Exchange ◽  
Multi Objective ◽  
Mathematical Problems ◽  
Pareto Fronts ◽  
Single Objective

Abstract In the present paper, a physics-inspired metaheuristic algorithm is presented to solve multi-objective optimization problems. The algorithm is developed based on the concept of Newtonian cooling law that recently has been employed by the Thermal Exchange Optimization (TEO) algorithm to efficiently solve single-objective optimization problems. The performance of the multi-objective version of TEO (MOTEO) is examined through bi- and tri-objective mathematical problems as well as bi-objective structural design examples. According to the comparisons between the MOTEO and several well-known algorithms, the proposed algorithm can provide quality Pareto fronts with appropriate accuracy, uniformity and coverage for multi-objective problems.

scholarly journals D2MOPSO: MOPSO Based on Decomposition and Dominance with Archiving Using Crowding Distance in Objective and Solution Spaces

2014 ◽  
Vol 22 (1) ◽  
pp. 47-77 ◽  
Author(s):  
N. Al Moubayed ◽  
A. Petrovski ◽  
J. McCall
Keyword(s):  
Optimization Problems ◽  
State Of The Art ◽  
Statistical Tests ◽  
Test Problems ◽  
Multi Objective Optimization ◽  
Improved Method ◽  
Particle Swarm Optimizer ◽  
Multi Objective ◽  
Wide Range ◽  
Comparison And Analysis

This paper improves a recently developed multi-objective particle swarm optimizer ([Formula: see text]) that incorporates dominance with decomposition used in the context of multi-objective optimization. Decomposition simplifies a multi-objective problem (MOP) by transforming it to a set of aggregation problems, whereas dominance plays a major role in building the leaders’ archive. [Formula: see text] introduces a new archiving technique that facilitates attaining better diversity and coverage in both objective and solution spaces. The improved method is evaluated on standard benchmarks including both constrained and unconstrained test problems, by comparing it with three state of the art multi-objective evolutionary algorithms: MOEA/D, OMOPSO, and dMOPSO. The comparison and analysis of the experimental results, supported by statistical tests, indicate that the proposed algorithm is highly competitive, efficient, and applicable to a wide range of multi-objective optimization problems.

scholarly journals Non-dominated sorting Harris’s hawk multi-objective optimizer based on reference point approach

2019 ◽  
Vol 15 (3) ◽  
pp. 1603 ◽  
Author(s):  
Shaymah Akram Yasear ◽  
Ku Ruhana Ku-Mahamud
Keyword(s):  
Reference Point ◽  
Pareto Front ◽  
Optimization Problems ◽  
Population Diversity ◽  
Experimental Results ◽  
Multi Objective Optimization ◽  
Next Generation ◽  
Multi Objective ◽  
Objective Space ◽  
Reference Point Approach

A non-dominated sorting Harris’s hawk multi-objective optimizer (NDSHHMO) algorithm is presented in this paper. The algorithm is able to improve the population diversity, convergence of non-dominated solutions toward the Pareto front, and prevent the population from trapping into local optimal. This was achieved by integrating fast non-dominated sorting with the original Harris’s hawk multi-objective optimizer (HHMO).  Non-dominated sorting divides the objective space into levels based on fitness values and then selects non-dominated solutions to produce the next generation of hawks. A set of well-known multi-objective optimization problems has been used to evaluate the performance of the proposed NDSHHMO algorithm. The results of the NDSHHMO algorithm were verified against the results of an HHMO algorithm. Experimental results demonstrate the efficiency of the proposed NDSHHMO algorithm in terms of enhancing the ability of convergence toward the Pareto front and significantly improve the search ability of the HHMO.

scholarly journals Modelling Diversity of Solutions

2020 ◽  
Vol 34 (02) ◽  
pp. 1528-1535
Author(s):  
Linnea Ingmar ◽  
Maria Garcia de la Banda ◽  
Peter J. Stuckey ◽  
Guido Tack
Keyword(s):  
General Framework ◽  
Optimization Problems ◽  
Pareto Frontier ◽  
Combinatorial Problem ◽  
Combinatorial Problems ◽  
Optimal Solutions ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Single Solution ◽  
Single Objective

For many combinatorial problems, finding a single solution is not enough. This is clearly the case for multi-objective optimization problems, as they have no single “best solution” and, thus, it is useful to find a representation of the non-dominated solutions (the Pareto frontier). However, it also applies to single objective optimization problems, where one may be interested in finding several (close to) optimal solutions that illustrate some form of diversity. The same applies to satisfaction problems. This is because models usually idealize the problem in some way, and a diverse pool of solutions may provide a better choice with respect to considerations that are omitted or simplified in the model. This paper describes a general framework for finding k diverse solutions to a combinatorial problem (be it satisfaction, single-objective or multi-objective), various approaches to solve problems in the framework, their implementations, and an experimental evaluation of their practicality.

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