Improved Random Drift Particle Swarm Optimization With Self-Adaptive Mechanism for Solving the Power Economic Dispatch Problem

El-Sayed, Wael T.; Hegazy, Yasser G.; ElBages, Mohamed S.; Bendary, Fahmy

doi:10.1109/tii.2017.2695122

Cited by 98 publications

(57 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Table 3 shows the comparison of the statistical results of different algorithms. In the table, the results obtained by BLPSO are compared with CLPSO, SLPSO, NPSO-LRS [54], MTS [55], TS [55], SA [55], GAAPI [56], HCRO-DE [42], DE [57], MABC [31], CBA [58], RDPSO [26], IRDPSO [26], and ST-IRDPSO [26]. It can be seen that the minimum and mean fuel costs obtained by BLPSO are similar to SLPSO and less than all the other methods with the exceptions of 10 Complexity HCRO-DE [42].…”

Section: Resultsmentioning

confidence: 99%

“…The second refers to improved or modified methods derived from the original version, and the following are included: self-adaptive real-coded genetic algorithm (SARGA) [24], random drift PSO (RDPSO) [25,26], fuzzy adaptive modified PSO (FAMPSO) [27], improved differential evolution (IDE) [28], shuffled differential evolution (SDE) [29], improved harmony search (IHS) [30], modified artificial bee colony (MABC) [31], incremental artificial bee colony (IABC) [32], ramp-rate biogeography-based optimization (RRBBO) [33], dynamic nondominated sorting biogeography-based optimization (Dy-NSBBO) [34], multistrategy ensemble biogeography-based optimization (MsEBBO) [35], and modified group search optimizer (MGSO) [36].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

2018

Complexity

View full text Add to dashboard Cite

Economic dispatch (ED) plays an important role in power system operation, since it can decrease the operating cost, save energy resources, and reduce environmental load. This paper presents an improved particle swarm optimization called biogeographybased learning particle swarm optimization (BLPSO) for solving the ED problems involving different equality and inequality constraints, such as power balance, prohibited operating zones, and ramp-rate limits. In the proposed BLPSO, a biogeographybased learning strategy is employed in which particles learn from each other based on the quality of their personal best positions, and thus it can provide a more efficient balance between exploration and exploitation. The proposed BLPSO is applied to solve five ED problems and compared with other optimization techniques in the literature. Experimental results demonstrate that the BLPSO is a promising approach for solving the ED problems.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

2018

Complexity

View full text Add to dashboard Cite

show abstract

“…These zones should be avoided to economize the power production. The prohibited operating zones can be expressed in the following inequality: (17) where P PZ and P PZ are the lower and upper limits of the prohibited zone for the ith generating unit, respectively.…”

Section: Prohibited Operating Zonesmentioning

confidence: 99%

“…As one of the widely used SI algorithms, PSO is easy to implement and converges fast. Since its inception, PSO has attracted great interest from the evolutionary computation (EC) community, and theoretical 2 of 27 researchers [10][11][12][13], and real-life applications [14][15][16][17][18] of PSO have been reported over the past two decades. Zhang et al [19] have summarized the recent advancements in PSO.…”

Section: Introductionmentioning

confidence: 99%

Multi-Leader Comprehensive Learning Particle Swarm Optimization with Adaptive Mutation for Economic Load Dispatch Problems

Lin

Sun

2018

Energies

View full text Add to dashboard Cite

Particle swarm optimization (PSO) is one of the most popular, nature inspired optimization algorithms. The canonical PSO is easy to implement and converges fast, however, it suffers from premature convergence. The comprehensive learning particle swarm optimization (CLPSO) can achieve high exploration while it converges relatively slowly on unimodal problems. To enhance the exploitation of CLPSO without significantly impairing its exploration, a multi-leader (ML) strategy is combined with CLPSO. In ML strategy, a group of top ranked particles act as the leaders to guide the motion of the whole swarm. Each particle is randomly assigned with an individual leader and the leader is refreshed dynamically during the optimization process. To activate the stagnated particles, an adaptive mutation (AM) strategy is introduced. Combining the ML and the AM strategies with CLPSO simultaneously, the resultant algorithm is referred to as multi-leader comprehensive learning particle swarm optimization with adaptive mutation (ML-CLPSO-AM). To evaluate the performance of ML-CLPSO-AM, the CEC2017 test suite was employed. The test results indicate that ML-CLPSO-AM performs better than ten popular PSO variants and six other types of representative evolutionary algorithms and meta-heuristics. To validate the effectiveness of ML-CLPSO-AM in real-life applications, ML-CLPSO-AM was applied to economic load dispatch (ELD) problems.

show abstract

“…Zou et al [64] used the new displacement update formula to guide the particle's search activity, expanding the particle's search space and reducing the possibility of particles falling into local optimum. Elsayed et al [65] added a crossover operation followed by a greedy selection process while replacing the mean best position of the particles with the personal best position of each particle in the velocity updating equation of random drift particle swarm, which increases the diversity of population and improves the performance of the algorithm. Yao et al [66] introduced quantum computing theory and used quantum bit and angle to depict the state of particles rather than using particle position and velocity of the classical PSO, which shows a stronger search ability and quicker convergence speed.…”

Section: Introductionmentioning

confidence: 99%

Modified Antipredatory Particle Swarm Optimization for Dynamic Economic Dispatch with Wind Power

Chen

Han

Wang

et al. 2019

Mathematical Problems in Engineering

View full text Add to dashboard Cite

A modified antipredatory particle swarm optimization (MAPSO) algorithm with evasive adjustment behavior is proposed to solve the dynamic economic dispatch problem of wind power. The algorithm adds the social avoidance inertia weight to the conventional antipredatory particle swarm optimization (APSO) speed update formula. The size of inertia weight is determined by the distance between the global worst particle and other particles. After normalizing the distance, the inertia weight is controlled within the ideal range by using the characteristics of sigmoid function and linear decreasing method, which improves the ability of particles to avoid the worst solution. Then, according to the characteristics of the acceleration coefficient which can adjust the local and global searching ability of particles, acceleration coefficients of nonlinear change strategy is proposed to improve the searching ability of the algorithm. Finally, the proposed algorithm is applied to several benchmark functions and power grid system models, and the results are compared with those reported using other algorithms, which prove the effectiveness and superiority of the proposed algorithm.

show abstract

Improved Random Drift Particle Swarm Optimization With Self-Adaptive Mechanism for Solving the Power Economic Dispatch Problem

Cited by 98 publications

References 41 publications

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

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

Multi-Leader Comprehensive Learning Particle Swarm Optimization with Adaptive Mutation for Economic Load Dispatch Problems

Modified Antipredatory Particle Swarm Optimization for Dynamic Economic Dispatch with Wind Power

Contact Info

Product

Resources

About