A Hybrid Multiobjective Particle Swarm Optimization Algorithm Based on R2 Indicator

Wei, Lixin; Li, Xin; Fan, Rui; Sun, Hao; Hu, Ziyu

doi:10.1109/access.2018.2812701

Cited by 38 publications

(23 citation statements)

References 28 publications

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“…Finally, we compare MOWOATS with hybrid variants of SI-based optimizers that use the R2 indicator to assess the quality of the solutions regarding the PF. We chose (R2HMOPSO, R2 indicator combined with MOPSO), R2HMOPSO1, MOEA/D, NSGA-II, dMOPSO , and R2MOPSO and, as in previous cases, computed the IGD (27) after solving the problems in the benchmarks ZDT (two objectives) and DTLZ (three-objective functions) [15]. The values appear in Table 7.…”

Section: Resultsmentioning

confidence: 99%

“…The hypervolume [25] and the R2 [26] indicators are two recommended approaches. The work in [15] follows this idea, and presents a hybrid multi-objective optimization algorithm which combines PSO with R2. It turns out that this achieves a better performance than using the meta-heuristics alone.…”

Section: Related Workmentioning

confidence: 99%

“…Many population-based methods have been combined with PO to solve MOPs, see for example Non-dominated Sorting Genetic Algorithm (NSGA) [9], NSGA-II [10], Multi-Objective Particle Swarm Optimization (MOPSO) [11], Multi-Objective Cat Swarm Optimization (MOCSO) [12], and Multi-Objective Grey Wolf Optimizer (MOGWO) [13], to name a few.Yet although SI methods presented a good performance in solving MOPs, they have their own drawbacks. Focusing on methods, such as Particle Swarm Optimization (PSO) and its rivals, these methods share common drawbacks such as selecting the swarm leader [14], getting trapped in local optima [15], and obtaining solutions that are not evenly distributed over solution space [15]. These problems result from the lack of mature memory elements, since only a single best solution within the search space is kept, which limits their capabilities to cover the whole solution space.…”

mentioning

confidence: 99%

“…Yet although SI methods presented a good performance in solving MOPs, they have their own drawbacks. Focusing on methods, such as Particle Swarm Optimization (PSO) and its rivals, these methods share common drawbacks such as selecting the swarm leader [14], getting trapped in local optima [15], and obtaining solutions that are not evenly distributed over solution space [15]. These problems result from the lack of mature memory elements, since only a single best solution within the search space is kept, which limits their capabilities to cover the whole solution space.…”

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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: 99%

Section: Related Workmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

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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“…In this article, (PSO) algorithm is used to tuning the input and output scaling factors of the controller. To optimize the output powers (Ps & Qs) of the wind turbine through the T2 fuzzy controller, it is required to properly tune the input and output gains of the controller [45][46][47]. Under such conditions, each of the input and output scaling factors of the type-2 controller will have a suitable number, in which its numerical amounts are determined by PSO algorithm.…”

Section: Tuning Of Ft2 Controller's Gains Using the Pso Algorithmmentioning

confidence: 99%

Ocean Wind Energy Technologies in Modern Electric Networks: Opportunity and Challenges

Gandoman¹,

Ahmadi²,

Aleem³

et al. 2020

Advances in Modelling and Control of Wind and Hydrogenerators

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Wind energy is one of the most important sources of energy in the world. In recent decades, wind as one of the massive marine energy resources in the ocean to produce electricity has been used. This chapter introduces a comprehensive overview of the efficient ocean wind energy technologies, and the global wind energies in both offshore and onshore sides are discussed. Also, the classification of global ocean wind energy resources is presented. Moreover, different components of a wind farm offshore as well as the technologies used in them are investigated. Possible layouts regarding the foundation of an offshore wind turbine, floating offshore, as well as the operation of wind farms in the shallow and deep location of the ocean are studied. Finally, the offshore wind power plant challenges are described.

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Research on hyper‐level of hyper‐heuristic framework for MOTCP

Guo,

Wang,

Han

et al. 2023

Software Testing Verif & Rel

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SummaryHeuristic algorithms are widely used to solve multi‐objective test case prioritization (MOTCP) problems. However, they perform differently for different test scenarios, which conducts difficulty in applying a suitable algorithm for new test requests in the industry. A concrete hyper‐heuristic framework for MOTCP (HH‐MOTCP) is proposed for addressing this problem. It mainly has two parts: low‐level encapsulating various algorithms and hyper‐level including an evaluation and selection mechanism that dynamically selects low‐level algorithms. This framework performs good but still difficult to keep in the best three. If the evaluation mechanism can more accurately analyse the current results, it will help the selection strategy to find more conducive algorithms for evolution. Meanwhile, if the selection strategy can find a more suitable algorithm for the next generation, the performance of the HH‐MOTCP framework will be better. In this paper, we first propose new strategies for evaluating the current generation results, then perform an extensive study on the selection strategies which decide the heuristic algorithm for the next generation. Experimental results show that the new evaluation and selection strategies proposed in this paper can make the HH‐MOTCP framework more effective and efficient, which makes it almost the best two except for one test object and ahead in about 40% of all test objects.

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A Hybrid Multiobjective Particle Swarm Optimization Algorithm Based on R2 Indicator

Cited by 38 publications

References 28 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

Ocean Wind Energy Technologies in Modern Electric Networks: Opportunity and Challenges

Research on hyper‐level of hyper‐heuristic framework for MOTCP

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