Dynamic mentoring and self-regulation based particle swarm optimization algorithm for solving complex real-world optimization problems

Tanweer, Muhammad Rizwan; Sundaram, Suresh; Sundararajan, N.

doi:10.1016/j.ins.2015.07.035

Cited by 92 publications

(23 citation statements)

References 61 publications

(124 reference statements)

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“…The parameters setting for this experiment were conducted with 50 eco-size and 500,000 fitness evaluations over 100 runs as same of reference [20]. The experimental results are compared with seven state-of-the-art PSO variants (FIPS [26], UPSO [25], CLPSO [18], χ PSO [28], BBPSO [29], DMSPSO [30], and DMeSR-PSO [20]) and five metaheuristic algorithms (GA [5], BBO [8], DE [9], PSMBA [31] and ABC [32]). For each function, the performance results are compared in terms of median, mean and standard deviation of all the algorithms.…”

Section: Experimental Results Of Cec2005 Benchmark Functionmentioning

confidence: 99%

“…Table 9 illustrates the experimental results of six (F1-F6) CEC 2005 benchmark functions for dimension 50. In Table 9, results except proposed HSOS are taken from references [20]. From Table 9, it can be detected that for function F1, F4 and F6, HSOS performs better than other algorithms; for function F2 and F3 the best result provides by the DMeSR-PSO method and for F5, provides the best result by DE method.…”

Section: Experimental Results Of Cec2005 Benchmark Functionmentioning

confidence: 99%

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A Hybrid Symbiosis Organisms Search algorithm and its application to real world problems

2016

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In this paper, a new hybrid algorithm, Hybrid Symbiosis Organisms Search (HSOS) has been proposed by combining Symbiosis Organisms Search (SOS) algorithm with Simple Quadratic Interpolation (SQI). The proposed algorithm provides more efficient behavior when dealing with real-world and large scale problems. To verify the performance of this suggested algorithm, 13 (Thirteen) well known benchmark functions, CEC2005 and CEC2010 special session on real-parameter optimization are being considered. The results obtained by the proposed method are compared with other state-of-the-art algorithms and it was observed that the suggested approach provides an effective and efficient solution in regards to the quality of the final result as well as the convergence rate. Moreover, the effect of the common controlling parameters of the algorithm, viz. population size, number of fitness evaluations (number of generations) of the algorithm are also being investigated by considering different population sizes and the number of fitness evaluations (number of generations). Finally, the method endorsed in this paper has been applied to two real life problems and it was inferred that the output of the proposed algorithm is satisfactory.

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Section: Experimental Results Of Cec2005 Benchmark Functionmentioning

confidence: 99%

Section: Experimental Results Of Cec2005 Benchmark Functionmentioning

confidence: 99%

A Hybrid Symbiosis Organisms Search algorithm and its application to real world problems

2016

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“…The test results prove MLPSO-STP performs better than classical PSO variants on selected benchmark functions. Tanweer et al [1] divided the whole swarm into mentors, independents and mentees according to the particle's fitness value and the Euclidian distance between the particle and Gbest. The high-quality mentors guide low quality mentees and the manner of learning different dimensions from different particle is adopted.…”

Section: The Social Learning Leadermentioning

confidence: 99%

“…Traditional optimization methods such as least square approximation, gradient descent and Newton methods belong to single point optimizers and need gradient information. Hence, most of the traditional optimizers are unfit for complex multimodal problems and non-differentiable optimization problems [1]. To cope with complex optimization problems, several swarm intelligence (SI) algorithms such as particle swarm optimization (PSO) [2,3], ant colony optimization (ACO) [4], artificial bee colony (ABC) [5], cuckoo search algorithm (CS) [6,7], grey wolf optimizer (GWO) [8], et al have been proposed over the past decades.…”

Section: Introductionmentioning

confidence: 99%

“…A lot of PSO variants with novel learning strategies have been proposed recently. For example, inspired by learning principles found in human cognitive psychology, Tanweer et al [1,39,40] proposed self-regulating particle swarm optimization (SRPSO). In SRPSO, a self-regulating inertia weight is employed by the best particle for better exploration, and self-perception of the global search direction is employed by the rest of particles for intelligent exploitation of the solution space.…”

Section: Introductionmentioning

confidence: 99%

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

Lin

Sun

2018

Energies

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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.

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An innovative hybrid algorithm to solve nonconvex economic load dispatch problem with or without valve point effects

Parouha

Verma²

2020

Int Trans Electr Energ Syst

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Dynamic mentoring and self-regulation based particle swarm optimization algorithm for solving complex real-world optimization problems

Cited by 92 publications

References 61 publications

A Hybrid Symbiosis Organisms Search algorithm and its application to real world problems

A Hybrid Symbiosis Organisms Search algorithm and its application to real world problems

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

An innovative hybrid algorithm to solve nonconvex economic load dispatch problem with or without valve point effects

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