Improved Harris's Hawk Multi-objective Optimizer Using Two-steps Initial Population Generation Method

2019 ◽  
Author(s):  
Shaymah Akram Yasear ◽  
Ku Ruhana Ku-Mahamud
Keyword(s):  
Initial Population ◽  
Multi Objective

scholarly journals Regional Integrated Energy Site Layout Optimization Based on Improved Artificial Immune Algorithm

Energies ◽  
2020 ◽  
Vol 13 (17) ◽  
pp. 4381
Author(s):  
Yan Xu ◽  
Jianhao Zhang
Keyword(s):  
Decision Making ◽  
Load Capacity ◽  
Layout Optimization ◽  
Immune Algorithm ◽  
Initial Population ◽  
Artificial Immune ◽  
Weighting Method ◽  
Artificial Immune Algorithm ◽  
Multi Objective ◽  
Site Layout

Regional integrated energy site layout optimization involves multi-energy coupling, multi-data processing and multi-objective decision making, among other things. It is essentially a kind of non-convex multi-objective nonlinear programming problem, which is very difficult to solve by traditional methods. This paper proposes a decentralized optimization and comprehensive decision-making planning strategy and preprocesses the data information, so as to reduce the difficulty of solving the problem and improve operational efficiency. Three objective functions, namely the number of energy stations to be built, the coverage rate and the transmission load capacity of pipeline network, are constructed, normalized by linear weighting method, and solved by the improved p-median model to obtain the optimal value of comprehensive benefits. The artificial immune algorithm was improved from the three aspects of the initial population screening mechanism, population updating and bidirectional crossover-mutation, and its performance was preliminarily verified by test function. Finally, an improved artificial immune algorithm is used to solve and optimize the regional integrated energy site layout model. The results show that the strategies, models and methods presented in this paper are feasible and can meet the interest needs and planning objectives of different decision-makers.

scholarly journals Improved NSGA-III using transfer learning and centroid distance for dynamic multi-objective optimization

2021 ◽  
Author(s):  
Haijuan Zhang ◽  
Gai-Ge Wang
Keyword(s):  
Transfer Learning ◽  
Pareto Front ◽  
Optimization Problems ◽  
Prediction Method ◽  
Reference Points ◽  
Initial Population ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Centroid Distance ◽  
Dynamic Strategy

AbstractMulti-objective problems in real world are often contradictory and even change over time. As we know, how to find the changing Pareto front quickly and accurately is challenging during the process of solving dynamic multi-objective optimization problems (DMOPs). In addition, most solutions obey different distributions in decision space and the performance of NSGA-III when dealing with DMOPs should be further improved. In this paper, centroid distance is proposed and combined into NSGA-III with transfer learning together for DMOPs, called TC_NSGAIII. Centroid distance-based strategy is regarded as a prediction method to prevent some inappropriate individuals through measuring the distance of the population centroid and reference points. After the distance strategy, transfer learning is used for generating an initial population using the past experience. To verify the effectiveness of our proposed algorithm, NSGAIII, Tr_NSGAIII (NSGA-III combining with transfer learning only), Ce_NSGAIII (NSGA-III combining with centroid distance only), and TC_NSGAIII are compared. Seven state-of-the-art algorithms have been used for comparison on CEC 2015 benchmarks. Besides, transfer learning and centroid distance are regarded as a dynamic strategy, which is incorporated into three static algorithms, and the performance improvement is measured. What’s more, twelve benchmark functions from CEC 2015 and eight sets of parameters in each function are used in our experiments. The experimental results show that the performance of algorithms can be greatly improved through the proposed approach.

Some Preliminary Results on the Development and Comparison of a New Multi-Objective Genetic Algorithm

2001 ◽  
Author(s):  
A. Farhang-Mehr ◽  
J. Wu ◽  
S. Azarm
Keyword(s):  
Genetic Algorithm ◽  
Optimization Problem ◽  
Quality Metrics ◽  
Initial Population ◽  
Multi Objective Optimization ◽  
Pareto Solutions ◽  
Preliminary Results ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm ◽  
Speed Reducer

Abstract Some preliminary results for a new multi-objective genetic algorithm (MOGA) are presented. This new algorithm aims at obtaining the fullest possible representation of observed Pareto solutions to a multi-objective optimization problem. The algorithm, hereafter called entropy-based MOGA (or E-MOGA), is based on an application of the concepts from the statistical theory of gases to a MOGA. A few set quality metrics are introduced and used for a comparison of the E-MOGA to a previously published MOGA. Due to the stochastic nature of the MOGA, confidence intervals with a 95% confidence level are calculated for the quality metrics based on the randomness in the initial population. An engineering example, namely the design of a speed reducer is used to demonstrate the performance of E-MOGA when compared to the previous MOGA.

scholarly journals Evolutionary Multi-Objective Energy Production Optimization: An Empirical Comparison

2020 ◽  
Vol 25 (2) ◽  
pp. 32 ◽  
Author(s):  
Gustavo-Adolfo Vargas-Hákim ◽  
Efrén Mezura-Montes ◽  
Edgar Galván
Keyword(s):  
Pareto Front ◽  
Power Production ◽  
Production Optimization ◽  
Production Costs ◽  
Initial Population ◽  
Nsga Ii ◽  
Power Sources ◽  
Multi Objective ◽  
Conflicting Objectives ◽  
Chaotic Model

This work presents the assessment of the well-known Non-Dominated Sorting Genetic Algorithm II (NSGA-II) and one of its variants to optimize a proposed electric power production system. Such variant implements a chaotic model to generate the initial population, aiming to get a better distributed Pareto front. The considered power system is composed of solar, wind and natural gas power sources, being the first two renewable energies. Three conflicting objectives are considered in the problem: (1) power production, (2) production costs and (3) CO2 emissions. The Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D) is also adopted in the comparison so as to enrich the empirical evidence by contrasting the NSGA-II versions against a non-Pareto-based approach. Spacing and Hypervolume are the chosen metrics to compare the performance of the algorithms under study. The obtained results suggest that there is no significant improvement by using the variant of the NSGA-II over the original version. Nonetheless, meaningful performance differences have been found between MOEA/D and the other two algorithms.

A New Artificial Immune Algorithm for Flexible Job-Shop Scheduling

2010 ◽  
Vol 121-122 ◽  
pp. 266-270
Author(s):  
Lu Hong
Keyword(s):  
Job Shop ◽  
Job Shop Scheduling ◽  
Immune Algorithm ◽  
Initial Population ◽  
Clonal Selection Algorithm ◽  
Scheduling Problem ◽  
Artificial Immune ◽  
Artificial Immune Algorithm ◽  
Shop Scheduling ◽  
Multi Objective

Flexible job-sop scheduling problem (FJSP) is based on the classical job-shop scheduling problem (JSP). however, it is even harder than JSP because of the addition of machine selection process in FJSP. An improved artificial immune algorithm, which combines the stretching technique and clonal selection algorithm is proposed to solve the FJSP. The algorithm can keep workload balance among the machines, improve the quality of the initial population and accelerate the speed of the algorithm’s convergence. The details of implementation for the multi-objective FJSP and the corresponding computational experiments are reported. The results indicate that the proposed algorithm is an efficient approach for the multi-objective FJSP.

scholarly journals Multi-objective Optimization of Data Placement in a Storage-as-a-Service Federated Cloud

2021 ◽  
Vol 17 (3) ◽  
pp. 1-32
Author(s):  
Amina Chikhaoui ◽  
Laurent Lemarchand ◽  
Kamel Boukhalfa ◽  
Jalil Boukhobza
Keyword(s):  
Execution Time ◽  
Service Providers ◽  
Data Placement ◽  
Exact Method ◽  
Initial Population ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Large Sets ◽  
Proposed Model ◽  
Injection Function

Cloud federation enables service providers to collaborate to provide better services to customers. For cloud storage services, optimizing customer object placement for a member of a federation is a real challenge. Storage, migration, and latency costs need to be considered. These costs are contradictory in some cases. In this article, we modeled object placement as a multi-objective optimization problem. The proposed model takes into account parameters related to the local infrastructure, the federated environment, customer workloads, and their SLAs. For resolving this problem, we propose CDP-NSGAII IR , a Constraint Data Placement matheuristic based on NSGAII with Injection and Repair functions. The injection function aims to enhance the solutions’ quality. It consists to calculate some solutions using an exact method then inject them into the initial population of NSGAII. The repair function ensures that the solutions obey the problem constraints and so prevents from exploring large sets of unfeasible solutions. It reduces drastically the execution time of NSGAII. Experimental results show that the injection function improves the HV of NSGAII and the exact method by up to 94% and 60%, respectively, while the repair function reduces the execution time by an average of 68%.

Towards a Methodology for Multidisciplinary Design Optimization of Haptic Devices

2015 ◽  
Author(s):  
Xuan Sun ◽  
Kjell Andersson ◽  
Ulf Sellgren
Keyword(s):  
Design Optimization ◽  
Multidisciplinary Design Optimization ◽  
Haptic Device ◽  
Optimization Process ◽  
Multidisciplinary Design ◽  
Medical Applications ◽  
Initial Population ◽  
Haptic Devices ◽  
Multi Objective ◽  
Computer Aided

Design of haptic devices requires trade-off between many conflicting requirements, such as high stiffness, large workspace, small inertia, low actuator force/torque, and a small size of the device. With the traditional design and optimization process, it is difficult to effectively fulfill the system requirements by separately treating the different discipline domains. To solve this problem and to avoid sub-optimization, this work proposes a design methodology, based on Multidisciplinary Design Optimization (MDO) methods and tools, for design optimization of six degree-of-freedom (DOF) haptic devices for medical applications, e.g. simulators for surgeon and dentist training or for remote surgery. The proposed model-based and simulation-driven methodology aims to enable different disciplines and subsystems to be included in the haptic device optimization process by using a robust model architecture that integrates discipline-specific models in an optimization framework and thus enables automation of design activities in the concept and detail design phase. Because of the multi-criteria character of the performance requirements, multi-objective optimization is included as part of the proposed methodology. Because of the high-level requirements on haptic devices for medical applications in combination with a complex structure, models such as CAD (Computer Aided Design), CAE (Computer Aided Engineering), and kinematic models are considered to be integrated in the optimization process and presenting a systems view to the design engineers. An integration tool for MDO is used as framework to manage, integrate, and execute the optimization process. A case study of a 6-DOF haptic device based on a TAU structure is used to illustrate the proposed methodology. With this specific case, a Multi-objective Genetic Algorithm (MOGA) with an initial population based on a pseudo random SOBOL sequence and Monte Carlo samplings is used for the optimization.

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