A region search evolutionary algorithm for many-objective optimization

Liu, Yongqi; Hui, Qing; Zhang, Zhendong; Yao, Liqiang; Wang, Chao; Li, Mo; Ouyang, Shan; Li, Jie

doi:10.1016/j.ins.2019.03.016

Cited by 31 publications

(15 citation statements)

References 35 publications

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“…Diversity based algorithms mitigate the unfavorable effects of diversity preservation. Some many-objective algorithms using a diversity-based approach are region search evolutionary algorithm [19], and NSGA-II+SDE [20]. Aggregation based algorithms rank solutions by aggregating individual information or pairwise information.…”

Section: A Multi-objective and Many-objective Optimizationmentioning

confidence: 99%

A Many-Objective Memetic Generalized Differential Evolution Algorithm for DNA Sequence Design

2020

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Designing reliable sequences of DNA (Deoxyribonucleic Acid) is a critical task in the fields of DNA computing, and nanotechnology. The quality and reliability of the DNA sequence can directly affect the accuracy of the processing of information stored in sequences. This problem of designing reliable sequences belongs to the NP-hard class of problems. It has many incompatible design criteria, which cannot be optimized at the same time. Many objective evolutionary algorithms can balance conflicting design criteria by using a diverse population of solutions. This paper proposes an opposition-based Memetic Generalized Differential Evolution (MGDE3) to handle four conflicting design criteria for reliable DNA sequence design. Opposition-based learning and local search strategies are suggested to strengthen the explorative and exploitative properties of the proposed MGDE3. The proposed algorithm is bench-marked with small, medium, and large data sets against 7 highly-cited many-objective and multi-objective algorithms. Experimental results and statistical analysis reveal that MGDE3 significantly outperforms the compared algorithms. The proposed method generates reliable real-life sequences of DNA that are substantially better than the DNA sequences generated by other considered algorithms.

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Section: A Multi-objective and Many-objective Optimizationmentioning

confidence: 99%

A Many-Objective Memetic Generalized Differential Evolution Algorithm for DNA Sequence Design

2020

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“…In the NSCDE algorithm, the culture algorithm (CA) is used to improve the performance of algorithms by adding domain knowledge [22]. The most significant feature of the CA is belief space where different kinds of knowledge can be obtained from the process of evolutionary and population space where individuals are updated according to a certain algorithm [23]. Non-dominated sorting genetic algorithm II (NSGA-II) [24] is used as the population space of the CA and normative knowledge and situational knowledge are used as the belief space of the CA.…”

Section: Non-dominated Sorting Culture Differential Evolution (Nscde)mentioning

confidence: 99%

Multi-Objective Optimal Scheduling Model of Dynamic Control of Flood Limit Water Level for Cascade Reservoirs

Liu

Hui

Shen

et al. 2019

Water

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Reservoirs play a significant role in water resources management and water resource allocation. Traditional flood limited water level (FLWL) of reservoirs is set as a fixed value which over-considers the reservoir flood control and limits the benefits of reservoirs to a certain extent. However, the dynamic control of the reservoir FLWL is an effective solution. It is a method to temporarily increase the water level of the reservoir during the flood season by using forecast information and discharge capacity, and it can both consider flood control and power generation during the flood season. Therefore, this paper focuses on multi-objective optimal scheduling of dynamic control of FLWL for cascade reservoirs based on multi-objective evolutionary algorithm to get the trade-off between flood control and power generation. A multi-objective optimal scheduling model of dynamic control of FLWL for cascade reservoirs which contains a new dynamic control method is developed, and the proposed model consists of an initialization module, a dynamic control programming module and an optimal scheduling module. In order to verify the effectiveness of the model, a cascade reservoir consisting of seven reservoirs in the Hanjiang Basin of China were selected as a case study. Twenty-four-hour runoff data series for three typical hydrological years were used in this model. At the same time, two extreme schemes were chosen for comparison from optimized scheduling schemes. The comparison result showed that the power generation can be increased by 9.17 × 108 kW·h (6.39%) at most, compared to the original design scheduling scheme, while the extreme risk rate also increased from 0.1% to 0.268%. In summary, experimental results show that the multi-objective optimal scheduling model established in this study can provide decision makers with a set of alternative feasible optimized scheduling schemes by considering the two objectives of maximizing power generation and minimizing extreme risk rate.

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“…Achieving a balance between convergence and diversity in many-objective optimization is a great challenge. Liu et al suggest an evolutionary algorithm based on a region search strategy that enhances the diversity of the population without losing convergence [92]. A hybrid evolutionary algorithm based on knee points and reference vector adaptation strate-gies (KnRVEA) is proposed to improve the convergence of solution where a novel knee adaptation strategy is introduced to adjust the distribution of knee points [93].…”

Section: Introductionmentioning

confidence: 99%

A comparative study of social group optimization with a few recent optimization algorithms

Naik

Satapathy

2020

Complex Intell. Syst.

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From the past few decades, the popularity of meta-heuristic optimization algorithms is growing compared to deterministic search optimization algorithms in solving global optimization problems. This has led to the development of several optimization algorithms to solve complex optimization problems. But none of the algorithms can solve all optimization problems equally well. As a result, the researchers focus on either improving exiting meta-heuristic optimization algorithms or introducing new algorithms. The social group optimization (SGO) Algorithm is a meta-heuristic optimization algorithm that was proposed in the year 2016 for solving global optimization problems. In the literature, SGO is shown to perform well as compared to other optimization algorithms. This paper attempts to compare the performance of the SGO algorithm with other optimization algorithms proposed between 2017 and 2019. These algorithms are tested through several experiments, including multiple classical benchmark functions, CEC special session functions, and six classical engineering problems etc. Optimization results prove that the SGO algorithm is extremely competitive as compared to other algorithms.

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A region search evolutionary algorithm for many-objective optimization

Cited by 31 publications

References 35 publications

A Many-Objective Memetic Generalized Differential Evolution Algorithm for DNA Sequence Design

A Many-Objective Memetic Generalized Differential Evolution Algorithm for DNA Sequence Design

Multi-Objective Optimal Scheduling Model of Dynamic Control of Flood Limit Water Level for Cascade Reservoirs

A comparative study of social group optimization with a few recent optimization algorithms

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