An Improved Multiobjective Quantum‐Behaved Particle Swarm Optimization Based on Double Search Strategy and Circular Transposon Mechanism

Han, Fei; Sun, Yu-Wen-Tian; Ling, Qing-Hua

doi:10.1155/2018/8702820

Cited by 14 publications

(16 citation statements)

References 33 publications

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“…The diversity of solutions obtained by the algorithm can also be guaranteed by using external archive. However, when the archive is full, as was adopted in [11], [12], [16], there are some problems with the traditional approach of maintaining external archive by directly eliminating particles with the smallest crowding distance. One solution that is considered crowded may become less crowded when other nearby solutions that cause its crowding are eliminated.…”

Section: The Modified Double-archive Mechanismmentioning

confidence: 99%

“…The conventional leader selection approach in [11], [12], [16] is to select the particle with larger crowding distance in the external archive. This is certainly feasible, it can avoid the solutions obtained by the algorithm to concentrate in a certain region, and thus the diversity of the solutions can be guaranteed.…”

Section: A Novel Leader Selection Strategymentioning

confidence: 99%

“…Furthermore, a competitive mechanism based multiobjective particle swarm optimizer was proposed in [15], where the particles were updated based on the pairwise competitions in the current swarm instead of the global or personal best particles. In [16], a double search strategy was adopted to balance the exploration and exploitation ability of the swarm. The circular transposon mechanism was also applied to improve the accuracy of the solutions set.…”

Section: Introductionmentioning

confidence: 99%

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A Modified Multi-Objective Particle Swarm Optimization Based on Levy Flight and Double-Archive Mechanism

Guan

Han

2019

IEEE Access

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In the past few decades, multi-objective particle swarm optimization (PSO) has increasingly attracted attention from scientists. To obtain a set of more accurate and well-distributed solutions, many variations of multi-objective PSO algorithms have been proposed. However, for complicated multi-objective problems, existing multi-objective PSO algorithms are prone to falling into local optima because of their weak global search capability. In this study, a modified multi-objective particle swarm optimization algorithm based on levy flight and double-archive mechanism (MOPSO-LFDA) is proposed to alleviate this problem. On one hand, in the evolution process of the particles, levy flight is combined with PSO to avoid the algorithm falling into local optima. By expanding the search scope of the particles, levy flight can improve the global search ability of the particles and make them jump out of local optima with a high probability. On the other hand, when maintaining external archives, in addition to the primary external archive, a secondary external archive is created to avoid unnecessary removal of the particles that may be generated by traditional maintenance approaches. With the proposed double-archive mechanism, more useful particles can be kept, and thus the diversity of the solutions is increased. Moreover, in terms of accelerating the convergence rate, a novel leader selection strategy is presented, which selects particles closer to the boundary of the attainable objective set and with larger crowding distance as leaders in optimization. The proposed algorithm outperforms existing state-of-the-art multi-objective algorithms on benchmark test functions for its fast convergence and excellent accuracy.

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Section: The Modified Double-archive Mechanismmentioning

confidence: 99%

Section: A Novel Leader Selection Strategymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Modified Multi-Objective Particle Swarm Optimization Based on Levy Flight and Double-Archive Mechanism

Guan

Han

2019

IEEE Access

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“…, }), its T closest weight vectors are assigned as its neighbors ( ) in lines (3)-(5), where 1 ≤ ≤ . Then, the ideal point will be initialized by finding the best value of each objective and the nadir point will be initialized using the corner solutions in lines (6)- (7), which have been introduced in Section 3.1. Afterward, like other MOEA/Ds [19][20][21][22][23][24][25], parent solutions are randomly selected from the neighborhood with a high probability in line (11).…”

Section: Generation Of Multiple Utopian Points In Existing Moe-mentioning

confidence: 99%

“…Compared with traditional mathematical methods, MOEAs can well solve various kinds of MOPs without their differentiable and derivable characteristics. Due to these advantages, MOEAs have been very popular in recent years [7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Evolutionary Search with Multiple Utopian Reference Points in Decomposition‐Based Multiobjective Optimization

Lin

Zhu

et al. 2019

Complexity

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Decomposition-based multiobjective evolutionary algorithms (MOEA/Ds) have become increasingly popular in recent years. In these MOEA/Ds, evolutionary search is guided by the used weight vectors in decomposition function to approximate the Pareto front (PF). Generally, the decomposition function will be constructed by the weight vectors and the reference point, which play an important role to balance convergence and diversity during the evolutionary search. However, in most existing MOEA/Ds, only one ideal point is used as the reference point for all the evolutionary search, which is harmful to search the entire PF when tackling the problems with difficult-to-approximate PF boundaries. To address the above problem, this paper proposes an evolutionary search method with multiple utopian reference points in MOEA/Ds. Similar to the existing MOEA/Ds, each solution is associated with one weight vector, which provides an evolutionary search direction, while the novelty of our approach is to use multiple utopian reference points, which can provide evolutionary search directions for different weight vectors. Corner solutions are used to approximate the nadir point and then multiple utopian reference points for evolutionary search can be constructed based on the ideal point and the nadir point, which are uniformly distributed on the coordinate axis or planes. The use of these utopian points can prevent solutions to gather in the same region of PF and helps to strike a good balance of exploration and exploitation in the search space. The performance of our proposed algorithm is validated on tackling 16 recently proposed test problems with difficult-to-approximate PF boundaries and empirically compared to eight state-of-the-art multiobjective evolutionary algorithms. The experimental results demonstrate the superiority of the proposed algorithm on solving most of the test problems adopted.

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A hybrid multiobjective optimization technique for optimal sizing of BESS‐WtE supported multi‐MW HRES to overcome ramp rate limitations on thermal stations

Nuvvula

Elangovan

Kishore³

2021

Int Trans Electr Energ Syst

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In India, favorable renewable energy policies have resulted in a higher proportion of renewable energy in the system. However, this led to the lower capacity utilization factor of thermal power stations. Any additional burden on thermal stations by enforcing higher ramp rates during peak hours would seriously affect technically as well as economically. In this regard, further renewable additions must be supported by energy storage systems to meet the ramping requirements. Wasteto-energy (WtE) plants play a crucial role in improving grid resilience in a highrenewable energy scenario to support expensive battery energy storage systems.In this article, the potential of WtE is assessed for a smart city in India, selected based on the possible urban and industrial growth, along with three other renewable energy technologies such as floating solar, bifacial rooftops solar, and wind energy conversion systems. The selected location has a total renewable energy potential of 439 MW. To achieve the municipality's techno-economic objectives, a multiobjective-enabled adaptive local attractor-based quantum-behaved particle swarm optimization (ALA-mQPSO) is proposed with mutation. With the proposed ALA-mQPSO, Pareto optimal sets of the hybrid renewable energy sources aided by the combination of battery energy storage system and WtE plant is achieved. The Pareto fronts are then compared with the benchmark techniques

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An Improved Multiobjective Quantum‐Behaved Particle Swarm Optimization Based on Double Search Strategy and Circular Transposon Mechanism

Cited by 14 publications

References 33 publications

A Modified Multi-Objective Particle Swarm Optimization Based on Levy Flight and Double-Archive Mechanism

A Modified Multi-Objective Particle Swarm Optimization Based on Levy Flight and Double-Archive Mechanism

Evolutionary Search with Multiple Utopian Reference Points in Decomposition‐Based Multiobjective Optimization

A hybrid multiobjective optimization technique for optimal sizing of BESS‐WtE supported multi‐MW HRES to overcome ramp rate limitations on thermal stations

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