scholarly journals A Novel Multi-Objective Five-Elements Cycle Optimization Algorithm

Algorithms ◽  
2019 ◽  
Vol 12 (11) ◽  
pp. 244 ◽  
Author(s):  
Chunling Ye ◽  
Zhengyan Mao ◽  
Mandan Liu
Keyword(s):  
Optimization Algorithm ◽  
Test Suite ◽  
Nsga Ii ◽  
Pareto Solution ◽  
Multi Objective ◽  
Chinese Traditional Culture ◽  
Knee Point ◽  
Five Elements ◽  
Fish Group ◽  
The Individual

Inspired by the mechanism of generation and restriction among five elements in Chinese traditional culture, we present a novel Multi-Objective Five-Elements Cycle Optimization algorithm (MOFECO). During the optimization process of MOFECO, we use individuals to represent the elements. At each iteration, we first divide the population into several cycles, each of which contains several individuals. Secondly, for every individual in each cycle, we judge whether to update it according to the force exerted on it by other individuals in the cycle. In the case of an update, a local or global update is selected by a dynamically adjustable probability P s ; otherwise, the individual is retained. Next, we perform combined mutation operations on the updated individuals, so that a new population contains both the reserved and updated individuals for the selection operation. Finally, the fast non-dominated sorting method is adopted on the current population to obtain an optimal Pareto solution set. The parameters’ comparison of MOFECO is given by an experiment and also the performance of MOFECO is compared with three classic evolutionary algorithms Non-dominated Sorting Genetic Algorithm II (NSGA-II), Multi-Objective Particle Swarm Optimization algorithm (MOPSO), Pareto Envelope-based Selection Algorithm II (PESA-II) and two latest algorithms Knee point-driven Evolutionary Algorithm (KnEA) and Non-dominated Sorting and Local Search (NSLS) on solving test function sets Zitzler et al’s Test suite (ZDT), Deb et al’s Test suite (DTLZ), Walking Fish Group (WFG) and Many objective Function (MaF). The experimental results indicate that the proposed MOFECO can approach the true Pareto-optimal front with both better diversity and convergence compared to the five other algorithms.

scholarly journals Multi-Objective Multidisciplinary Design Optimization of a Robotic Fish System

2021 ◽  
Vol 9 (5) ◽  
pp. 478
Author(s):  
Hao Chen ◽  
Weikun Li ◽  
Weicheng Cui ◽  
Ping Yang ◽  
Linke Chen
Keyword(s):  
Design Optimization ◽  
Optimization Algorithm ◽  
Multidisciplinary Design Optimization ◽  
Robotic Fish ◽  
Multidisciplinary Design ◽  
Optimization Approach ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Cfd Method ◽  
The Individual

Biomimetic robotic fish systems have attracted huge attention due to the advantages of flexibility and adaptability. They are typically complex systems that involve many disciplines. The design of robotic fish is a multi-objective multidisciplinary design optimization problem. However, the research on the design optimization of robotic fish is rare. In this paper, by combining an efficient multidisciplinary design optimization approach and a novel multi-objective optimization algorithm, a multi-objective multidisciplinary design optimization (MMDO) strategy named IDF-DMOEOA is proposed for the conceptual design of a three-joint robotic fish system. In the proposed IDF-DMOEOA strategy, the individual discipline feasible (IDF) approach is adopted. A novel multi-objective optimization algorithm, disruption-based multi-objective equilibrium optimization algorithm (DMOEOA), is utilized as the optimizer. The proposed MMDO strategy is first applied to the design optimization of the robotic fish system, and the robotic fish system is decomposed into four disciplines: hydrodynamics, propulsion, weight and equilibrium, and energy. The computational fluid dynamics (CFD) method is employed to predict the robotic fish’s hydrodynamics characteristics, and the backpropagation neural network is adopted as the surrogate model to reduce the CFD method’s computational expense. The optimization results indicate that the optimized robotic fish shows better performance than the initial design, proving the proposed IDF-DMOEOA strategy’s effectiveness.

Comparison of Multi-Objective Evolutionary Algorithms to Prioritize Regression Test Cases

2021 ◽  
Vol 9 (12) ◽  
pp. 1445-1454
Keyword(s):  
Fault Detection ◽  
Evolutionary Algorithms ◽  
Fitness Function ◽  
Optimal Solution ◽  
Test Suite ◽  
Test Case ◽  
Test Cases ◽  
Nsga Ii ◽  
Multi Objective ◽  
Regression Test

Regression testing is one of the most critical testing activities among software product verification activities. Nevertheless, resources and time constraints could inhibit the execution of a full regression test suite, hence leaving us in confusion on what test cases to run to preserve the high quality of software products. Different techniques can be applied to prioritize test cases in resource-constrained environments, such as manual selection, automated selection, or hybrid approaches. Different Multi-Objective Evolutionary Algorithms (MOEAs) have been used in this domain to find an optimal solution to minimize the cost of executing a regression test suite while obtaining maximum fault detection coverage as if the entire test suite was executed. MOEAs achieve this by selecting set of test cases and determining the order of their execution. In this paper, three Multi Objective Evolutionary Algorithms, namely, NSGA-II, IBEA and MoCell are used to solve test case prioritization problems using the fault detection rate and branch coverage of each test case. The paper intends to find out what’s the most effective algorithm to be used in test cases prioritization problems, and which algorithm is the most efficient one, and finally we examined if changing the fitness function would impose a change in results. Our experiment revealed that NSGA-II is the most effective and efficient MOEA; moreover, we found that changing the fitness function caused a significant reduction in evolution time, although it did not affect the coverage metric.

Hybrid QPSO-NNIA2 Algorithm for Multi-Objective Optimization Problem

2019 ◽  
Vol 33 (08) ◽  
pp. 1959025
Author(s):  
Lan Zhang
Keyword(s):  
Optimization Algorithm ◽  
Optimization Problem ◽  
Nearest Neighbor ◽  
Superior Performance ◽  
Optimal Solutions ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Multi Objective ◽  
Evolution Algebra ◽  
Neighbor List

To improve the convergence and distribution of a multi-objective optimization algorithm, a hybrid multi-objective optimization algorithm, based on the quantum particle swarm optimization (QPSO) algorithm and adaptive ranks clone and neighbor list-based immune algorithm (NNIA2), is proposed. The contribution of this work is threefold. First, the vicinity distance was used instead of the crowding distance to update the archived optimal solutions in the QPSO algorithm. The archived optimal solutions are updated and maintained by using the dynamic vicinity distance based m-nearest neighbor list in the QPSO algorithm. Secondly, an adaptive dynamic threshold of unfitness function for constraint handling is introduced in the process. It is related to the evolution algebra and the feasible solution. Thirdly, a new metric called the distribution metric is proposed to depict the diversity and distribution of the Pareto optimal. In order to verify the validity and feasibility of the QPSO-NNIA2 algorithm, we compare it with the QPSO, NNIA2, NSGA-II, MOEA/D, and SPEA2 algorithms in solving unconstrained and constrained multi-objective problems. The simulation results show that the QPSO-NNIA2 algorithm achieves superior convergence and superior performance by three metrics compared to other algorithms.

Research on multi-objective optimization of switched flux motor based on improved NSGA-II algorithm

2019 ◽  
Vol 233 (6) ◽  
pp. 1268-1279 ◽  
Author(s):  
Liying Jin ◽  
Shengdun Zhao ◽  
Wei Du ◽  
Xuesong Yang ◽  
Wensheng Wang ◽  
...  
Keyword(s):  
Permanent Magnets ◽  
Optimal Solution ◽  
Torque Ripple ◽  
Maximum Output ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Multi Objective ◽  
Tooth Width ◽  
The Individual ◽  
Rotor Tooth

In order to optimize the local search efficiency of multi-objective parameters of flux switching permanent motor based on traditional NSGA-II algorithm, an improved NSGA-II (iNSGA-II) algorithm is proposed, with an anti-redundant mutation operator and forward comparison operation designed for quick identification of non-dominated individuals. In the initial stage of the iNSGA-II algorithm, half of the individual populations were randomly generated, while the other half was generated according to feature distribution information. Taking the flux switching permanent motor stator/rotor gap, permanent magnets width, stator tooth width, rotor tooth width and other parameters as optimization variables, the flux switching permanent motor maximum output shaft torque and minimum torque ripple are taken as optimization objectives, thus a multi-objective optimization model is established. Real number coding was adopted for obtaining the Pareto optimal solution of flux switching permanent motor structure parameters. The results showed that the iNSGA-II algorithm is better than the traditional NSGA-II on convergence. A 1.8L TOYOTA PRIUS model was selected as the prototype vehicle. By using the optimized parameters, a joint optimization simulation model was established by calling ADVISOR’s back-office function. The simulation results showed that the entire vehicle’s 100-km acceleration time is under 8 s and the battery’s SOC value maintains at 0.5–0.7 in the entire cycle, implying that the iNSGA-II algorithm optimizes the flux switching permanent motor design and is suitable for the initial design and optimizing calculation of the flux switching permanent motor.

Study of the Non-Dominated Sorting Genetic Algorithm for the Optimal Structural Parameter of the Tooth-Arrangement Multi-Fingered Dextrous Hand

2013 ◽  
Vol 756-759 ◽  
pp. 4082-4089
Author(s):  
Zhan Li Li ◽  
Xiang Ting He
Keyword(s):  
Genetic Algorithm ◽  
Parameter Optimization ◽  
Experimental Results ◽  
Structural Parameter ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Pareto Solution ◽  
Multi Objective ◽  
Dextrous Hand

Firstly, the structural parameter optimization of the tooth-arrangement multi-fingered dextrous hand is studied. Secondly, as to the shortcomings that the Pareto solution of multi-objective optimization was distributed unevenly in NSGA-II, a non-dominated sorting genetic algorithm based on immune principle is proposed. Lastly, the structural parameter of the medical tooth-arrangement multi-fingered dextrous hand is optimized using the proposed algorithm. The experimental results show that this algorithm can optimize structural parameter of tooth-arrangement multi-fingered dextrous hand very well.

scholarly journals Multi-objective Human-computer Co-operative Co-evolutionary Method Based on Non-dominated Sorting Strategy

2014 ◽  
Vol 8 (1) ◽  
pp. 213-217
Author(s):  
Huo Junzhou ◽  
Chen Jing ◽  
Zhou Jianjun ◽  
Wu Hanyang
Keyword(s):  
Numerical Simulation ◽  
Layout Problem ◽  
Pareto Solution ◽  
Trade Off ◽  
Multi Objective ◽  
Evolutionary Method ◽  
Complex Engineering ◽  
Cooperative Solutions ◽  
Simulation Results ◽  
The Individual

Based on the human-computer cooperation ideas, a Human-Computer Multi-Objective Cooperative Co-Evolutionary Method (HCMCCM) is developed to solve the complex engineering layout problem, in which the multiobjective optimization idea is integrated to avoid the "flooding" phenomenon that occurs during the combination of the artificial solutions and the algorithm solutions. In the proposed HCMCCM, the artificial solutions expressed by unified encoding strings are incorporated together with the algorithms solutions to create new cooperative solutions based on the non-dominated sorting strategies. This kind of cooperation can make the artificial solutions and the algorithm solutions on an equal basis and integrate the artificial individual with the individual algorithms into a multi-objective trade-off. The numerical simulation results of the satellite layout problem show that the proposed method can combine the artificial solutions and the algorithm solutions effectively and provide a Pareto solution set for engineers to choose from.

Applications of Multi-Objective Genetic Algorithm in Optimizing Steering Performances for Submersible

2013 ◽  
Vol 756-759 ◽  
pp. 3136-3140
Author(s):  
Zhuo Yi Yang ◽  
Yong Jie Pang ◽  
Shao Lian Ma
Keyword(s):  
Genetic Algorithm ◽  
Optimal Design ◽  
Approximation Model ◽  
Horizontal Movement ◽  
Nsga Ii ◽  
Hydrodynamic Coefficient ◽  
Pareto Solution ◽  
Multi Objective ◽  
Minimum Drag ◽  
Multi Objective Genetic Algorithm

Multi-objective arithmetic NSGA-II based on Pareto solution is investigated to deal with integrated optimal design of speedability and manoeuvre performances for submersible. Approximation model of resistance for serial revolving shape is constructed by hydrodynamic numerical calculations. The appraisement criterions of stability and mobility are calculated from linear equation of horizontal movement by estimating hydrodynamic coefficient of submersible. After optimization, the scattered Pareto solution of drag and turning diameter are gained, and from the solutions designer can select the reasonable one based on the actual requirement. The Pareto solution can ensure the minimum drag in this manoeuvre performance or the best manoeuvre performance in this drag value.

scholarly journals A multi-objective scheduling method for operational coordination time using improved triangular fuzzy number representation

PLoS ONE ◽  
2021 ◽  
Vol 16 (6) ◽  
pp. e0252293
Author(s):  
Luda Zhao ◽  
Bin Wang ◽  
Congyong Shen
Keyword(s):  
Fuzzy Number ◽  
Large Scale ◽  
Bat Algorithm ◽  
Triangular Fuzzy Number ◽  
Performance Criteria ◽  
Number Representation ◽  
Nsga Ii ◽  
Pareto Solution ◽  
Multi Objective ◽  
Time Scheduling

In modern warfare, the comprehensiveness of combat domain and the complexity of tasks pose great challenges to operational coordination.To address this challenge, we use the improved triangular fuzzy number to express the combat mission time, first present a new multi-objective operational cooperative time scheduling model that takes the fluctuation of combat coordinative time and the time flexibility between each task into account. The resulting model is essentially a large-scale multi-objective combinatorial optimization problem, intractably complicated to solve optimally. We next propose multi-objective improved Bat algorithm based on angle decomposition (MOIBA/AD) to quickly identify high-quality solutions to the model. Our proposed algorithm improves the decomposition strategy by replacing the planar space with the angle space, which helps greatly reduce the difficulty of processing evolutionary individuals and hence the time complexity of the multi-objective evolutionary algorithm based on decomposition (MOEA/D). Moreover, the population replacement strategy is enhanced utilizing the improved bat algorithm, which helps evolutionary individuals avoid getting trapped in local optima. Computational experiments on multi-objective operational cooperative time scheduling (MOOCTS) problems of different scales demonstrate the superiority of our proposed method over four state-of-the-art multi-objective evolutionary algorithms (MOEAs), including multi-objective bat Algorithm (MOBA), MOEA/D, non-dominated sorting genetic algorithm version II (NSGA-II) and multi-objective particle swarm optimization algorithm (MOPSO). Our proposed method performs better in terms of four performance criteria, producing solutions of higher quality while keeping a better distribution of the Pareto solution set.

scholarly journals A Novel Multi-Objective Artificial Bee Colony Algorithm for the QoS Based Wireless Route Optimization Problem

2012 ◽  
Vol 6-7 ◽  
pp. 445-451
Author(s):  
Chang Sheng Zhang ◽  
Ming Kang Ren ◽  
Bin Zhang
Keyword(s):  
Optimization Algorithm ◽  
Optimization Problem ◽  
Artificial Bee Colony ◽  
Route Optimization ◽  
Test Problems ◽  
Nsga Ii ◽  
Solution Quality ◽  
Artificial Bee Colony Optimization ◽  
Multi Objective ◽  
Bee Colony

In this paper, an efficient multi-objective artificial bee colony optimization algorithm based on Pareto dominance called PC_MOABC is proposed to tackle the QoS based route optimization problem. The concepts of Pareto strength and crowding distance are introduced into this algorithm, and are combined together effectively to improve the algorithm’s efficiency and generate a set of evenly distributed solutions. The proposed algorithm was evaluated on a set of different scale test problems and compared with the recently proposed popular NSGA-II based multi-objective optimization algorithm. The experimental results reveal very encouraging results in terms of the solution quality and the processing time required.

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