An Improved Particle Swarm Optimization with Biogeography‐Based Learning Strategy for Economic Dispatch Problems

Xu, Chunxiao; Xu, Bin; Du, Wenli

doi:10.1155/2018/7289674

Cited by 34 publications

(25 citation statements)

References 71 publications

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“…GLxBBO is an incomplete variant of ILxBBO, which is LxBBO with only the two-global-best guiding operator, without the dynamic two-differential perturbing operator and improved Laplace migration operator DLxBBO is an incomplete variant of ILxBBO, which is LxBBO with only the dynamic two-differential perturbing operator without the improved Laplace migration operator and two-global-best guiding operator OLxBBO is an incomplete variant of ILxBBO, which contains the improved Laplace migration operator without the dynamic two-differential perturbing operator and two-global-best guiding operator e experimental results are shown in Table 1 [39], BIBBO [25], BBOM [40], DEBBO [30], BLPSO [41], PRBBO [24], WRBBO [37], EMBBO [27], and BHCS [42]. ese algorithms are all BBO variants proposed in recent years, with much comparability.…”

Section: Comparison Of Ilxbbo With Its Incomplete Variantsmentioning

confidence: 99%

Improved Laplacian Biogeography-Based Optimization Algorithm and Its Application to QAP

et al. 2020

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Laplacian Biogeography-Based Optimization (LxBBO) is a BBO variant which improves BBO’s performance largely. When it solves some complex problems, however, it has some drawbacks such as poor performance, weak operability, and high complexity, so an improved LxBBO (ILxBBO) is proposed. First, a two-global-best guiding operator is created for guiding the worst habitat mainly to enhance the exploitation of LxBBO. Second, a dynamic two-differential perturbing operator is proposed for the first two best habitats’ updating to improve the global search ability in the early search phase and the local one in the late search one, respectively. Third, an improved Laplace migration operator is formulated for other habitats’ updating to improve the search ability and the operability. Finally, some measures such as example learning, mutation operation removing, and greedy selection are adopted mostly to reduce the computation complexity of LxBBO. A lot of experimental results on the complex functions from the CEC-2013 test set show ILxBBO obtains better performance than LxBBO and quite a few state-of-the-art algorithms do. Also, the results on Quadratic Assignment Problems (QAPs) show that ILxBBO is more competitive compared with LxBBO, Improved Particle Swarm Optimization (IPSO), and Improved Firefly Algorithm (IFA).

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Section: Comparison Of Ilxbbo With Its Incomplete Variantsmentioning

confidence: 99%

Improved Laplacian Biogeography-Based Optimization Algorithm and Its Application to QAP

et al. 2020

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“…(3) while G � 1 to Mg do (4) P(G + 1) � ∅; (5) while P(G) ≠ ∅ do (6) Generate two random indices r 1 and r 2 from np; (7) if…”

Section: Remarkmentioning

confidence: 99%

“…ere were an enormous number of studies on hybrid system optimization over the past twenty years. Advanced technologies have been applied to the economic power dispatch problem in hybrid energy systems [5][6][7]. Most of such studies focused on power ow control strategies where the demand side was rather considered constraints in the system.…”

Section: Introductionmentioning

confidence: 99%

A Competitive Swarm Optimizer‐Based Technoeconomic Optimization with Appliance Scheduling in Domestic PV‐Battery Hybrid Systems

Wang

Yang

et al. 2019

Complexity

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A technoeconomic optimization problem for a domestic grid-connected PV-battery hybrid energy system is investigated. It incorporates the appliance time scheduling with appliance-specific power dispatch. The optimization is aimed at minimizing energy cost, maximizing renewable energy penetration, and increasing user satisfaction over a finite horizon. Nonlinear objective functions and constraints, as well as discrete and continuous decision variables, are involved. To solve the proposed mixed-integer nonlinear programming problem at a large scale, a competitive swarm optimizer-based numerical solver is designed and employed. The effectiveness of the proposed approach is verified by simulation results.

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“…Another important topic is about the valve point effect [28][29][30][31][32][33][34][35][36][37][38][39][40][41]. Many meaningful works are focused on the valve point effect, such as evolutionary programming [28,29], genetic algorithm [30][31][32][33], and particle swarm optimisation [34,35].…”

Section: Introductionmentioning

confidence: 99%

Distributed Prescribed Finite Time Consensus Scheme for Economic Dispatch of Smart Grids with the Valve Point Effect

2020

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The distributed prescribed finite time consensus schemes for economic dispatch (ED) of smart grids with and without the valve point effect are researched in this paper. First, the optimization problem is transformed into a consensus of multiagent system problem, where both with and without the valve point effect are considered. Second, for the directed balance network, a prescribed finite time method has been arranged to solve the ED problem with and without the valve point effect. Third, with considering the constraints of generation units, the prescribed finite time result is also achieved. Finally, from the simulations, the efficiency of the proposed algorithms is validated.

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An Improved Particle Swarm Optimization with Biogeography‐Based Learning Strategy for Economic Dispatch Problems

Cited by 34 publications

References 71 publications

Improved Laplacian Biogeography-Based Optimization Algorithm and Its Application to QAP

Improved Laplacian Biogeography-Based Optimization Algorithm and Its Application to QAP

A Competitive Swarm Optimizer‐Based Technoeconomic Optimization with Appliance Scheduling in Domestic PV‐Battery Hybrid Systems

Distributed Prescribed Finite Time Consensus Scheme for Economic Dispatch of Smart Grids with the Valve Point Effect

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