A knee-guided prediction approach for dynamic multi-objective optimization

Zou, Fei; Yen, Gary G.; Tang, Lixin

doi:10.1016/j.ins.2019.09.016

Cited by 63 publications

(11 citation statements)

References 29 publications

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“…As shown in Figure 5, these knee solutions are generally characterized as the optimal solutions within the respective regions which can benefit from significant improvement in some objectives at the cost of insignificant degradations in the other objectives from a theoretical perspective. Compared with approaches aggregating weighted objectives into a single fitness function, the knee solutions reflect the optimal solution more accurately without considering multiple preferences of decision-makers [26]. The calculation method of knee point is listed in Formula (21).…”

Section: Resultsmentioning

confidence: 99%

UAV Path Planning Based on Multi-Stage Constraint Optimization

Shen

Zhu

Kang

et al. 2021

Drones

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Evolutionary Algorithms (EAs) based Unmanned Aerial Vehicle (UAV) path planners have been extensively studied for their effectiveness and high concurrency. However, when there are many obstacles, the path can easily violate constraints during the evolutionary process. Even if a single waypoint causes a few constraint violations, the algorithm will discard these solutions. In this paper, path planning is constructed as a multi-objective optimization problem with constraints in a three-dimensional terrain scenario. To solve this problem in an effective way, this paper proposes an evolutionary algorithm based on multi-level constraint processing (ANSGA-III-PPS) to plan the shortest collision-free flight path of a gliding UAV. The proposed algorithm uses an adaptive constraint processing mechanism to improve different path constraints in a three-dimensional environment and uses an improved adaptive non-dominated sorting genetic algorithm (third edition—ANSGA-III) to enhance the algorithm’s path planning ability in a complex environment. The experimental results show that compared with the other four algorithms, ANSGA-III-PPS achieves the best solution performance. This not only validates the effect of the proposed algorithm, but also enriches and improves the research results of UAV path planning.

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Section: Resultsmentioning

confidence: 99%

UAV Path Planning Based on Multi-Stage Constraint Optimization

Shen

Zhu

Kang

et al. 2021

Drones

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“…Then the Euclidean distance between the centroid point and the reference point can be calculated by Eq. (7).…”

Section: The Centroid Distance Methodsmentioning

confidence: 99%

“…For the prediction method, some specific strategies are employed to predict the next environmental change, so that the population can be evolved towards the right direction, greatly reducing the possibility of reverse evolution. A novel knee point-based prediction approach was proposed by Zou et al [7]. It maintained the non-dominated solutions around the knee regions and the burden of maintaining diversity and convergence at the same time was reduced for the most part.…”

Section: Introductionmentioning

confidence: 99%

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

Zhang

Wang

2021

Complex Intell. Syst.

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Multi-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.

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“…Besides, with the L 1 metric, as demonstrated in Section 3, TOPSIS guarantees that the proposed solution is classified with respect to both the ideal I + and nadir I − solutions even if these are not known. It could also be deduced from the results in Figure 18 that the solution achieved by ELECTRE I resides or is close to a knee region [62][63][64][65][66][67][68] where a small improvement in one of the objectives leads to a significant degradation in at least one of the other objectives, and therefore it may be of more interest to a DM than the solution calculated by TOPSIS. In any case, the selection of the best MCDM method for a given problem can be a difficult task [69], and it is not within the scope of this work.…”

Section: Three-objective Welded Beam Design Problem (Decision)mentioning

confidence: 99%

TOPSIS Decision on Approximate Pareto Fronts by Using Evolutionary Algorithms: Application to an Engineering Design Problem

et al. 2020

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A common technique used to solve multi-objective optimization problems consists of first generating the set of all Pareto-optimal solutions and then ranking and/or choosing the most interesting solution for a human decision maker (DM). Sometimes this technique is referred to as generate first–choose later. In this context, this paper proposes a two-stage methodology: a first stage using a multi-objective evolutionary algorithm (MOEA) to generate an approximate Pareto-optimal front of non-dominated solutions and a second stage, which uses the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) devoted to rank the potential solutions to be proposed to the DM. The novelty of this paper lies in the fact that it is not necessary to know the ideal and nadir solutions of the problem in the TOPSIS method in order to determine the ranking of solutions. To show the utility of the proposed methodology, several original experiments and comparisons between different recognized MOEAs were carried out on a welded beam engineering design benchmark problem. The problem was solved with two and three objectives and it is characterized by a lack of knowledge about ideal and nadir values.

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A knee-guided prediction approach for dynamic multi-objective optimization

Cited by 63 publications

References 29 publications

UAV Path Planning Based on Multi-Stage Constraint Optimization

UAV Path Planning Based on Multi-Stage Constraint Optimization

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

TOPSIS Decision on Approximate Pareto Fronts by Using Evolutionary Algorithms: Application to an Engineering Design Problem

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