A new hybrid memetic multi-objective optimization algorithm for multi-objective optimization

Luo, Jianping; Yang, Yun; Liu, Qiqi; Li, Xia; Chen, Min-Rong; Gao, Kaizhou

doi:10.1016/j.ins.2018.03.012

Cited by 30 publications

(9 citation statements)

References 33 publications

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“…However, most of the above MOIAs only use simple hypermutation operators to evolve antibodies [12,36,42,43]. The use of simple evolutionary methods in MOIAs may lead to monotonous search patterns, which makes existing MOIAs unable to deal with complex MOPs (for example, the UF test problem [32]). In fact, the hybrid mutation method has been studied in immune algorithms [19,20,27,39], and good results have been achieved.…”

Section: Immunology Terms In Moiasmentioning

confidence: 99%

An adaptive hybrid evolutionary immune multi-objective algorithm based on uniform distribution selection

Qiao

Yang

et al. 2020

Information Sciences

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In general, for the iteration process of an evolutionary algorithm (EA), there exists the problem of uneven distribution of individuals in the target space for both multi-objective and single-objective optimization problems. This uneven distribution significantly degrades the population diversity and convergence speed. This paper proposes an adaptive hybrid evolutionary immune algorithm based on a uniform distribution selection mechanism (AUDHEIA) for solving MOPs efficiently. In AUDHEIA, the individuals in the population are mapped to a hyperplane, which is correlated with the objective space and are clustered to increase the diversity of solutions. To improve the distribution of the solutions, the mapped hyperplane is evenly sectioned. With the constantly changing distribution during the iteration, a threshold as a standard for judging the distribution level is adjusted adaptively. When the threshold is not satisfied in the corresponding interval, the distribution enhancement module is activated. Then, the same number of individuals should be selected in each interval. However, sometimes, there are insufficient or no individuals in the interval during the iterative process. To obtain sufficient individuals, the limit optimization variation strategy of the best individual is adopted. Experiments show that this algorithm can escape from local optima and has a high convergence speed. Moreover, the distribution and convergence of this algorithm are superior to the peer algorithms tested in this paper.

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Section: Immunology Terms In Moiasmentioning

confidence: 99%

An adaptive hybrid evolutionary immune multi-objective algorithm based on uniform distribution selection

Qiao

Yang

et al. 2020

Information Sciences

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“…In a rescheduling phase, iJaya is compared with two multi-objective optimization algorithms, i.e., a classical multi-objective algorithm, NSGAII [39], and a new published algorithm, improved shuffled frog leaping algorithm (ISFLA) [40]. For the real-life FJRP cases, nobody knows the actual Pareto front (PF) since the high complexity.…”

Section: Ijaya For Scheduling and Reschedulingmentioning

confidence: 99%

Improved Jaya Algorithm for Flexible Job Shop Rescheduling Problem

Gao

Yang

et al. 2020

IEEE Access

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Machine recovery is met from time to time in real-life production. Rescheduling is often a necessary procedure to cope with it. Its instability gauges the number of changes to the existing scheduling solutions. It is a key criterion to measure a rescheduling solution's quality. This work aims at solving a flexible job shop problem with machine recovery, which arises from the scheduling and rescheduling of pump remanufacturing systems. In their scheduling phase, the objective is to minimize makespan. In their rescheduling phase, two objectives are to minimize both instability and makespan. By introducing two novel local search operators into the original Jaya algorithm, this work proposes an improved Jaya algorithm to solve it. It performs experiments on ten different-scale cases of real-life remanufacturing environment. The results show that the improved Jaya is effective and efficient for solving a flexible job shop scheduling and rescheduling problems. It can effectively balance instability and makespan in a rescheduling phase. INDEX TERMS Jaya algorithm, flexible job shop scheduling, machine recovery, remanufacturing, scheduling and rescheduling.

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“…To test the performance of the proposed algorithm, the MNSGA-II is compared with three other heuristic based multi-objective optimization algorithms: ISFLA [25], MOVPSO [26] and HNDS [27]. The parameter values of MNSGA-II are given in Table 3.…”

Section: Simulation and Evaluation A Simulation Settingsmentioning

confidence: 99%

“…Moreover, in the optimization process, a dynamic depth search strategy is proposed to adjust the search scope in a timely manner and explore the newly discovered solution domain by reallocating the chromosome positions. Finally, the control allocation relationships on different flying quality levels are analysed and a range of simulations on the 6DoF Boeing 747 model are carried out to demonstrate the effectiveness of the proposed method over other heuristics based multi-objective optimization algorithms, namely ISFLA [25], MOVPSO [26] and HNDS [27].…”

Section: Introductionmentioning

confidence: 99%

A Modified NSGA-II for Solving Control Allocation Optimization Problem in Lateral Flight Control System for Large Aircraft

2019

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Coordinated aileron and rudder control is crucial to the lateral control stability augmentation of an aircraft. In this paper, a modified non-dominated sorting genetic algorithm II is proposed to not only optimize the control allocation between the aileron and rudder channels on different flying quality levels but also explore the relationships between the optimum solutions and the state variables of the aircraft. In doing so, a digital, nets-based stratification method is used to initialize the search chromosomes more evenly. To improve the search efficiency of the algorithm, crowding-distance-based interpolation and elimination strategies are developed to approach the optimum Pareto frontier as close as possible. Moreover, a dynamic depth search method is proposed to balance between the global and local explorations. Finally, the control allocation relationships between the aileron and rudder channels on different flying quality levels are illustrated. The comparative simulations on a six-degree-of-freedom Boeing 747 model are carried out to verify the feasibility of the proposed algorithm. INDEX TERMS MNSGA-II, control allocation optimization, flight control system.

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A new hybrid memetic multi-objective optimization algorithm for multi-objective optimization

Cited by 30 publications

References 33 publications

An adaptive hybrid evolutionary immune multi-objective algorithm based on uniform distribution selection

An adaptive hybrid evolutionary immune multi-objective algorithm based on uniform distribution selection

Improved Jaya Algorithm for Flexible Job Shop Rescheduling Problem

A Modified NSGA-II for Solving Control Allocation Optimization Problem in Lateral Flight Control System for Large Aircraft

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