A Clustering-Based Adaptive Evolutionary Algorithm for Multiobjective Optimization With Irregular Pareto Fronts

Hua, Yicun; Jin, Yaochu; Hao, Kuangrong

doi:10.1109/tcyb.2018.2834466

Cited by 137 publications

(42 citation statements)

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“…where P represents the true PF solutions, P * stands for the optimal solutions generated from the algorithm, and |P| is the number of the true PF. Specifically, Table 6 contains a comparison among MOWOATS, CA-MOEA, MOEA/D, EMyO/C, RVEA*, NSGA-II, and NSGA-III according to IGD metric computed according to Equation (27) [34]. The results confirm that MOWOATS reaches the lowest mean values of the IGD metric for all test functions, except for DTLZ73, for which it yields the second rank.…”

Section: Resultsmentioning

confidence: 70%

“…Another feature which strains the quality of solutions computed by a MOP solver occurs when the PF exhibits an irregular shape. There have been some works in the literature that address this issue in particular, such as the Clustering-based Adaptive Multi-Objective EA (CA-MOEA) by Hua et al [34], who suggested using a clustering-based method to overcome those problems with irregular PF shapes. The use of clustering helps to diversify the tentative solutions.…”

Section: Resultsmentioning

confidence: 99%

“…We also did an experimental comparison of this algorithm and other relatives/variants with the ours. MOWOATS is compared with this method according to IGD metric, computed as [34]:…”

Section: Resultsmentioning

confidence: 99%

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A Pareto-Based Hybrid Whale Optimization Algorithm with Tabu Search for Multi-Objective Optimization

et al. 2019

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Multi-Objective Problems (MOPs) are common real-life problems that can be found in different fields, such as bioinformatics and scheduling. Pareto Optimization (PO) is a popular method for solving MOPs, which optimizes all objectives simultaneously. It provides an effective way to evaluate the quality of multi-objective solutions. Swarm Intelligence (SI) methods are population-based methods that generate multiple solutions to the problem, providing SI methods suitable for MOP solutions. SI methods have certain drawbacks when applied to MOPs, such as swarm leader selection and obtaining evenly distributed solutions over solution space. Whale Optimization Algorithm (WOA) is a recent SI method. In this paper, we propose combining WOA with Tabu Search (TS) for MOPs (MOWOATS). MOWOATS uses TS to store non-dominated solutions in elite lists to guide swarm members, which overcomes the swarm leader selection problem. MOWOATS employs crossover in both intensification and diversification phases to improve diversity of the population. MOWOATS proposes a new diversification step to eliminate the need for local search methods. MOWOATS has been tested over different benchmark multi-objective test functions, such as CEC2009, ZDT, and DTLZ. Results present the efficiency of MOWOATS in finding solutions near Pareto front and evenly distributed over solution space.

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Section: Resultsmentioning

confidence: 70%

Section: Resultsmentioning

confidence: 99%

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A Pareto-Based Hybrid Whale Optimization Algorithm with Tabu Search for Multi-Objective Optimization

et al. 2019

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“…The Pareto optimal solutions of these problems are distributed only in part of the objective space. Several characteristics make such problems more difficult to solve than problems with regular Pareto fronts [5,6]. Firstly, since the distribution of Pareto front is unknown, it is hard to identify the real Pareto front area.…”

Section: Introductionmentioning

confidence: 99%

“…Firstly, algorithms based on clustering or space division. In our recent work [6], a clustering based multi-objective evolutionary algorithm called CA-MOEA was proposed, which adaptively generates a set of cluster centers in the current population as the reference points to maintain diversity and accelerate convergence for problems with irregular Pareto fronts. However, as CA-MOEA is mainly based on non-dominated sorting, it encounters resistance in dealing with many-objective problems.…”

Section: Introductionmentioning

confidence: 99%

Generating multiple reference vectors for a class of many-objective optimization problems with degenerate Pareto fronts

Hua

Jin

Hao

et al. 2020

Complex Intell. Syst.

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Many-objective optimization problems with degenerate Pareto fronts are hard to solve for most existing many-objective evolutionary algorithms. This is particularly true when the shape of the degenerate Pareto front is very narrow, and there are many dominated solutions near the Pareto front. To solve this particular class of many-objective optimization problems, a new evolutionary algorithm is proposed in this paper. In this algorithm, a set of reference vectors is generated to locate the potential Pareto front and then generate a set of location vectors. With the help of the location vectors, the solutions near the Pareto front are mapped to the hyperplane and clustered to generate more reference vectors pointing to Pareto front. This way, the location vectors are able to efficiently guide the population to converge towards the Pareto front. The effectiveness of the proposed algorithm is examined on two typical test problems with degenerate Pareto fronts, namely DTLZ5 and DTLZ6 with 5-40 objectives. Our experimental results show that the proposed algorithm has a clear advantage in dealing with this class of many-objective optimization problems. In addition, the proposed algorithm has also been successfully applied to optimization of process parameters of polyester fiber filament melt-transportation.

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A GSO‐based multi‐objective technique for performance optimization of blockchain‐based industrial Internet of things

Zanbouri,

Darbandi,

Nassr

et al. 2024

Int J Communication

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SummaryThe latest developments in the industrial Internet of things (IIoT) have opened up a collection of possibilities for many industries. To solve the massive IIoT data security and efficiency problems, a potential approach is considered to satisfy the main needs of IIoT, such as high throughput, high security, and high efficiency, which is named blockchain. The blockchain mechanism is considered a significant approach to boosting data protection and performance. In the quest to amplify the capabilities of blockchain‐based IIoT, a pivotal role is accorded to the Glowworm Swarm Optimization (GSO) algorithm. Inspired by the collaborative brilliance of glowworms in nature, the GSO algorithm offers a unique approach to harmonizing these conflicting aims. This paper proposes a new approach to improve the performance optimization of blockchain‐based IIoT using the GSO algorithm due to the blockchain's contradictory objectives. The proposed blockchain‐based IIoT system using the GSO algorithm addresses scalability challenges typically associated with blockchain technology by efficiently managing interactions among nodes and dynamically adapting to network demands. The GSO algorithm optimizes the allocation of resources and decision‐making, reducing inefficiencies and bottlenecks. The method demonstrates considerable performance improvements through extensive simulations compared to traditional algorithms, offering a more scalable and efficient solution for industrial applications in the context of the IIoT. The extensive simulation and computational study have shown that the proposed method using GSO considerably improves the objective function and blockchain‐based IIoT systems' performance compared to traditional algorithms. It provides more efficient and secure systems for industries and corporations.

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A Clustering-Based Adaptive Evolutionary Algorithm for Multiobjective Optimization With Irregular Pareto Fronts

Cited by 137 publications

References 50 publications

A Pareto-Based Hybrid Whale Optimization Algorithm with Tabu Search for Multi-Objective Optimization

A Pareto-Based Hybrid Whale Optimization Algorithm with Tabu Search for Multi-Objective Optimization

Generating multiple reference vectors for a class of many-objective optimization problems with degenerate Pareto fronts

A GSO‐based multi‐objective technique for performance optimization of blockchain‐based industrial Internet of things

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