Multi-Objective Cuckoo Search Under Multiple Archiving Strategies

2016 ◽  
Vol 15 (04) ◽  
pp. 1650020 ◽  
Author(s):  
Kamel Zeltni ◽  
Souham Meshoul
Keyword(s):  
Performance Metrics ◽  
Cuckoo Search ◽  
Nonparametric Test ◽  
Critical Issue ◽  
Test Problems ◽  
Nondominated Solutions ◽  
Multi Objective ◽  
Bounded Set ◽  
Recent Addition

Cuckoo Search (CS) is a recent addition to the field of swarm-based metaheuristics. It has been shown to be an efficient approach for global optimization. Moreover, its application for solving Multi-objective Optimization (MOO) shows very promising results as well. In multi-objective context, a bounded archive is required to store the set of nondominated solutions. But, what is the best archiving strategy to use in order to maintain a bounded set with good characteristics is a critical issue that may lead to a questionable choice. In this work, the behavior of the developed multi-objective CS is studied under several archiving strategies. An extensive experimental study has been conducted using several test problems and two performance metrics related to convergence and diversity. A nonparametric test for statistical analysis is performed. In addition, we used a Multi-Objective Particle Swarm Optimization (MOPSO) for further analysis and comparison. The results revealed that archiving strategies play an important role as they can impact differently on the quality of obtained fronts depending on the problem’s characteristics. Also, this study confirms that the proposed MOCS algorithm is a very promising approach for MOPs compared to the widely used MOPSO.

On the Convergence and Diversity of Pareto Fronts Using Swarm Intelligence Metaheuristics for Constrained Search Space

2020 ◽  
pp. 1573-1593
Author(s):  
Kamel Zeltni ◽  
Souham Meshoul ◽  
Heyam H. Al-Baity
Keyword(s):  
Swarm Intelligence ◽  
Performance Metrics ◽  
Optimization Problems ◽  
Cuckoo Search ◽  
Search Space ◽  
Constraint Handling ◽  
Nsga Ii ◽  
Multi Objective ◽  
Handling Mechanism ◽  
Pareto Fronts

This article reviews existing constraint-handling techniques then presents a new design for Swarm Intelligence Metaheuristics (SIM) to deal with constrained multi-objective optimization problems (CMOPs). This new design aims to investigate potential effects of leader concepts that characterize the dynamic of SIM in the hope to help the population to reach Pareto optimal solutions in a constrained search space. The new leader-based constraint handling mechanism is incorporated in Constrained Multi-Objective Cuckoo Search (C-MOCS) and Constrained Multi-Objective Particle Swarm Optimization (C-MOPSO) as specific instances of a more general class of SIMs. The experimental results are carried out using a set of six well-known test functions and two performance metrics. The convergence and diversity of C-MOCS and C-MOPSO are analysed and compared to the well-known Multi-Objective Evolutionary Algorithm (MOEA) NSGA-II and discussed based on the obtained results.

scholarly journals Multi-objective Optimization of a Hybrid Nanogrid/Microgrid: Application to Desert Camps in Hafr Al-Batin

Energies ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 1245
Author(s):  
Houssem Rafik Al-Hana Bouchekara ◽  
Mohammad Shoaib Shahriar ◽  
Muhammad Sharjeel Javaid ◽  
Yusuf Abubakar Sha’aban ◽  
Makbul Anwari Muhammad Ramli
Keyword(s):  
Pareto Front ◽  
Operation Mode ◽  
Multi Objective Optimization ◽  
Nondominated Solutions ◽  
Multi Objective ◽  
Storage Batteries ◽  
The Cost ◽  
Component Models ◽  
The City

This paper presents an optimal design for a nanogrid/microgrid for desert camps in the city of Hafr Al-Batin in Saudi Arabia. The camps were designed to operate as separate nanogrids or to operate as an interconnected microgrid. The hybrid nanogrid/microgrid considered in this paper consists of a solar system, storage batteries, diesel generators, inverter, and load components. To offer the designer/operator various choices, the problem was formulated as a multi-objective optimization problem considering two objective functions, namely: the cost of electricity (COE) and the loss of power supply probability (LPSP). Furthermore, various component models were implemented, which offer a variety of equipment compilation possibilities. The formulated problem was then solved using the multi-objective evolutionary algorithm, based on both dominance and decomposition (MOEA/DD). Two cases were investigated corresponding to the two proposed modes of operation, i.e., nanogrid operation mode and microgrid operation mode. The microgrid was designed considering the interconnection of four nanogrids. The obtained Pareto front (PF) was reported for each case and the solutions forming this front were discussed. Based on this investigation, the designer/operator can select the most appropriate solution from the available set of solutions using his experience and other factors, e.g., budget, availability of equipment and customer-specific requirements. Furthermore, to assess the quality of the solutions found using the MOEA/DD, three different methods were used, and their results compared with the MOEA/DD. It was found that the MOEA/DD obtained better results (nondominated solutions), especially for the microgrid operation mode.

scholarly journals A new balanced direction for multi-objective evolutionary algorithms

2016 ◽  
Author(s):  
Nguyen Long ◽  
Nguyen Xuan Hung ◽  
Nguyen Thi Hien ◽  
Bui Thu Lam
Keyword(s):  
Evolutionary Algorithms ◽  
Performance Metrics ◽  
Test Problems ◽  
Evolutionary Processes ◽  
Multi Objective

This paper suggests to use a new improvement direction for multi-objective evolutionaryalgorithms. In DMEA-II, two improvement directions(convergence and spread) are used for the guidanceduring evolutionary processes. Based on those directions and the balance between exploration andexploitation, we determined a new improvement direction to keep DMEA-II to be better on the balanceof convergence and diversity.To validate the performance of the new improvedversion of DMEA-II, we carried out a case studyon several test problems and comparison with wellknown MOEAs, it obtained quite good results onprimary performance metrics, namely the generationdistance, inverse generation distance and hypervolume. Our analysis on the results indicates that,the usage of proposed direction may make DMEAII to be improved in keeping balanced betweenconvergence and diversity at each generation duringthe search

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.

IMPROVING THE PERFORMANCE OF MULTI-OBJECTIVE EVOLUTIONARY ALGORITHMS USING THE ISLAND PARALLEL MODEL

2007 ◽  
Vol 17 (02) ◽  
pp. 127-139 ◽  
Author(s):  
A. MÁRQUEZ ◽  
C. GIL ◽  
R. BAÑOS ◽  
J. GÓMEZ
Keyword(s):  
Parallel Processing ◽  
Evolutionary Algorithms ◽  
Population Size ◽  
Test Problems ◽  
Pareto Optimal ◽  
Parallel Model ◽  
Multi Objective Optimization ◽  
Pareto Optimal Front ◽  
Multi Objective

Recently, the research interest in multi-objective optimization has increased remarkably. Most of the proposed methods use a population of solutions that are simultaneously improved trying to approximate them to the Pareto-optimal front. When the population size increases, the quality of the solutions tends to be better, but the runtime is higher. This paper presents how to apply parallel processing to enhance the convergence to the Pareto-optimal front, without increasing the runtime. In particular, we present an island-based parallelization of five multi-objective evolutionary algorithms: NSGAII, SPEA2, PESA, msPESA, and a new hybrid version we propose. Experimental results in some test problems denote that the quality of the solutions tends to improve when the number of islands increases.

Multi-objective optimization of job shops with automated guided vehicles: A non-dominated sorting cuckoo search algorithm

2020 ◽  
pp. 1748006X2094653
Author(s):  
Behzad Karimi ◽  
Seyed Taghi Akhavan Niaki ◽  
Amir Hossein Niknamfar ◽  
Mahsa Gareh Hassanlu
Keyword(s):  
Manufacturing Systems ◽  
Production Efficiency ◽  
Performance Metrics ◽  
Job Shop ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Automated Guided Vehicles ◽  
Job Shops ◽  
Multi Objective

The reliability of machinery and automated guided vehicle has been one of the most important challenges to enhance production efficiency in several manufacturing systems. Reliability improvement would result in a simultaneous reduction of both production times and transportation costs of the materials, especially in automated guided vehicles. This article aims to conduct a practical multi-objective reliability optimization model for both automated guided vehicles and the machinery involved in a job-shop manufacturing system, where different machines and the storage area through some parallel automated guided vehicles handle materials, parts, and other production needs. While similar machines in each shop are limited to failures based on either an Exponential or a Weibull distribution via a constant rate, the machines in different shops fail based on different failure rates. Meanwhile, as the model does not contain any closed-form equation to measure the machine reliability in the case of Weibull failure, a simulation approach is employed to estimate the shop reliability to be further maximized using the proposed model. Besides, the automated guided vehicles are restricted to failures according to an Exponential distribution. Furthermore, choosing the best locations of the shops is proposed among some potential places. The proposed NP-Hard problem is then solved by designing a novel non-dominated sorting cuckoo search algorithm. Furthermore, a multi-objective teaching-learning-based optimization, as well as a multi-objective invasive weed optimization are designed to validate the results obtained. Ultimately, a novel AHP-TOPSIS method is carried out to rank the algorithms in terms of six performance metrics.

scholarly journals Application of nature inspired algorithms for multi-objective inventory control scenarios

2021 ◽  
pp. 91-114 ◽  
Author(s):  
Ferdous Sarwar ◽  
Mushaer Ahmed ◽  
Mahjabin Rahman
Keyword(s):  
Inventory Control ◽  
Heuristic Algorithm ◽  
Performance Metrics ◽  
Bat Algorithm ◽  
Raw Material ◽  
Test Problems ◽  
Multi Objective ◽  
Work In Process ◽  
Multi Objective Programming ◽  
Demand Rate

An inventory control system having multiple items in stock is developed in this paper to optimize total cost of inventory and space requirement. Inventory modeling for both the raw material storage and work in process (WIP) is designed considering independent demand rate of items and no volume discount. To make the model environmentally aware, the equivalent carbon emission cost is also incorporated as a cost function in the formulation. The purpose of this study is to minimize the cost of inventories and minimize the storage space needed. The inventory models are shown here as a multi-objective programming problem with a few nonlinear constraints which has been solved by proposing a meta-heuristic algorithm called multi-objective particle swarm optimization (MOPSO). A further meta-heuristic algorithm called multi-objective bat algorithm (MOBA) is used to determine the efficacy of the result obtained from MOPSO. Taguchi method is followed to tune necessary response variables and compare both algorithm's output. At the end, several test problems are generated to evaluate the performances of both algorithms in terms of six performance metrics and analyze them statistically and graphically.

On the Convergence and Diversity of Pareto Fronts Using Swarm Intelligence Metaheuristics for Constrained Search Space

2018 ◽  
Vol 9 (1) ◽  
pp. 20-38
Author(s):  
Kamel Zeltni ◽  
Souham Meshoul ◽  
Heyam H. Al-Baity
Keyword(s):  
Swarm Intelligence ◽  
Performance Metrics ◽  
Optimization Problems ◽  
Cuckoo Search ◽  
Search Space ◽  
Constraint Handling ◽  
Test Functions ◽  
Nsga Ii ◽  
Multi Objective ◽  
Pareto Fronts

This article reviews existing constraint-handling techniques then presents a new design for Swarm Intelligence Metaheuristics (SIM) to deal with constrained multi-objective optimization problems (CMOPs). This new design aims to investigate potential effects of leader concepts that characterize the dynamic of SIM in the hope to help the population to reach Pareto optimal solutions in a constrained search space. The new leader-based constraint handling mechanism is incorporated in Constrained Multi-Objective Cuckoo Search (C-MOCS) and Constrained Multi-Objective Particle Swarm Optimization (C-MOPSO) as specific instances of a more general class of SIMs. The experimental results are carried out using a set of six well-known test functions and two performance metrics. The convergence and diversity of C-MOCS and C-MOPSO are analysed and compared to the well-known Multi-Objective Evolutionary Algorithm (MOEA) NSGA-II and discussed based on the obtained results.

Patient satisfaction and comfort after a full-arch immediate loaded prosthesis

2020 ◽  
Author(s):  
Elena Dellepiane ◽  
Francesco Pera ◽  
Paola Zunino ◽  
Maria Grazia Mugno ◽  
Paolo Pesce ◽  
...  
Keyword(s):  
Quality Of Life ◽  
Oral Health ◽  
Intermediate Phase ◽  
Critical Issue ◽  
Health Impact ◽  
Immediate Loading ◽  
Oral Rehabilitation ◽  
Masticatory Ability ◽  
Chewing Ability

The aim of this study was to assess oral health related quality of life (OHRQoL) of patients before, during and after completion of implant-supported full-arch immediate loading rehabilitation according to the Columbus Bridge Protocol (CBP). 25 patients with compromised dentition were rehabilitated according to the CBP and were assessed for OHRQoL using 4 questionnaires specifically realized for this study and inspired to the OHIP (Oral Health Impact Profile) questionnaire. Patients assessed themselves before surgery, during the healing period (1 week and 2 months after surgery) and after definitive prosthodontic treatment (4 months after surgery). The questionnaires specifically investigated patients’ pain, confort, home oral hygiene habits, satisfaction related to esthetics, masticatory ability, phonetics and general satisfaction toward the treatment.Patients reported an improvement of OHRQoL after full-arch immediate loading rehabilitation. A statistically significant improvement in aesthetic and chewing ability was found. After 4 months 92% of the patients did not feel tense with their smile, 96% did not show problems to relate with other people or smiling, 92% did not show difficulty to eat some foods. Phonetics was found to be a critical issue, especially in the intermediate phase of healing. One week after surgery the percentage of patients who was very satisfied with phonetics slightly decreased from 48% to 36%. The assessment of patients' OHRQoL related to full-arch immediate loading implant therapy exhibited a significant improvement of their quality of life. The questionnaires herein presented could be an effective tool to evaluate patients' reaction to oral rehabilitation.

Classification of methods for generating pseudo-CT from MRI images for MRI-alone RT

2017 ◽  
Vol 1 (3) ◽  
pp. 54
Author(s):  
BOUKELLOUZ Wafa ◽  
MOUSSAOUI Abdelouahab
Keyword(s):  
Performance Metrics ◽  
Radiation Treatment ◽  
Hybrid Methods ◽  
Dose Calculation ◽  
Absolute Error ◽  
Ct Images ◽  
General Technique ◽  
Therapy Field ◽  
The Brain

Background: Since the last decades, research have been oriented towards an MRI-alone radiation treatment planning (RTP), where MRI is used as the primary modality for imaging, delineation and dose calculation by assigning to it the needed electron density (ED) information. The idea is to create a computed tomography (CT) image or so-called pseudo-CT from MRI data. In this paper, we review and classify methods for creating pseudo-CT images from MRI data. Each class of methods is explained and a group of works in the literature is presented in detail with statistical performance. We discuss the advantages, drawbacks and limitations of each class of methods. Methods: We classified most recent works in deriving a pseudo-CT from MR images into four classes: segmentation-based, intensity-based, atlas-based and hybrid methods. We based the classification on the general technique applied in the approach. Results: Most of research focused on the brain and the pelvis regions. The mean absolute error (MAE) ranged from 80 HU to 137 HU and from 36.4 HU to 74 HU for the brain and pelvis, respectively. In addition, an interest in the Dixon MR sequence is increasing since it has the advantage of producing multiple contrast images with a single acquisition. Conclusion: Radiation therapy field is emerging towards the generalization of MRI-only RT thanks to the advances in techniques for generation of pseudo-CT images. However, a benchmark is needed to set in common performance metrics to assess the quality of the generated pseudo-CT and judge on the efficiency of a certain method.

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