Attributed multi-objective comprehensive learning particle swarm optimization for optimal security of networks

Ali, Hamid; Khan, Farrukh Aslam

doi:10.1016/j.asoc.2013.04.015

Cited by 29 publications

(14 citation statements)

References 21 publications

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“…External elitist archive is used 18 to store the non-dominated solutions obtained by an algorithm and these obtained solutions are filtered by a certain quality 19 measure, such as density. There are several proposals for MPSO where the archive size is unconstrained [1,3], the pre-fixed 20 maximum size of an archive is widely applied because the number of non-dominated solutions can grow very fast, which quickly 21 increases the computation cost of updating the archive. Besides, the physical memory is always finite in size.…”

Section: Introductionmentioning

confidence: 99%

A new multi-objective particle swarm optimization algorithm based on decomposition

Dai

Wang

Miao

2015

Information Sciences

173

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Section: Introductionmentioning

confidence: 99%

A new multi-objective particle swarm optimization algorithm based on decomposition

Dai

Wang

Miao

2015

Information Sciences

173

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“…A series of simulation experiments have been conducted to examine the performance of MOMVO. Its results are compared with those that are provided in Ali and Khan . The proposed algorithm is compiled in MATLAB 2016R in a personal computer with Windows 10 operation system, an 8th Gen core i5 processor and 8GB RAM.…”

Section: Multi‐objective Multiverse Optimization Algorithm (Momvo)mentioning

confidence: 99%

Electric vehicles management enabling G2V and V2G in smart distribution system for maximizing profits using MOMVO

Kasturi

Nayak

2019

Int Trans Electr Energ Syst

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Summary Charging electric vehicles (EVs) by the grid leads to unexpected spikes in load demand threatening the power system health. As EVs store energy, they can dispense power to meet peak load demands. This paper proposes a charging/discharging strategy to augment the utility of the EVs with a controlled schedule by considering an appropriate driving pattern. EVs are allowed to charge or discharge at the workplace or home which are connected to the same bus of a radial distribution system. Multiobjective multiverse optimization algorithm (MOMVO) is utilized to extract the best possible number of EVs and the bus at which they must be connected, not only to minimize the impact of EV charging/discharging on the grid but also to reduce the costs associated with the operation on behalf of both the EV owner and the utility company. The results obtained ensure both technical and economical appositeness of the proposed strategy.

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“…Extending from the powerful single-objective PSO variant CLPSO, this paper proposes multiswarm CLPSO (MSCLPSO) for multiobjective optimization. CLPSO has been extended to handle multiobjective optimization in [ 13 , 20 ]. No decomposition is involved in multiobjective CLPSO (MOCLPSO) and attributed MOCLPSO (A-MOCLPSO) respectively proposed in [ 13 ] and [ 20 ].…”

Section: Introductionmentioning

confidence: 99%

“…CLPSO has been extended to handle multiobjective optimization in [ 13 , 20 ]. No decomposition is involved in multiobjective CLPSO (MOCLPSO) and attributed MOCLPSO (A-MOCLPSO) respectively proposed in [ 13 ] and [ 20 ]. MSCLPSO is the same with CMPSO [ 14 ] in terms of using multiple swarms, the way of determining the personal best position, and storing elitists in a shared external repository.…”

Section: Introductionmentioning

confidence: 99%

Multiswarm comprehensive learning particle swarm optimization for solving multiobjective optimization problems

Zhang

2017

PLoS ONE

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Comprehensive learning particle swarm optimization (CLPSO) is a powerful state-of-the-art single-objective metaheuristic. Extending from CLPSO, this paper proposes multiswarm CLPSO (MSCLPSO) for multiobjective optimization. MSCLPSO involves multiple swarms, with each swarm associated with a separate original objective. Each particle’s personal best position is determined just according to the corresponding single objective. Elitists are stored externally. MSCLPSO differs from existing multiobjective particle swarm optimizers in three aspects. First, each swarm focuses on optimizing the associated objective using CLPSO, without learning from the elitists or any other swarm. Second, mutation is applied to the elitists and the mutation strategy appropriately exploits the personal best positions and elitists. Third, a modified differential evolution (DE) strategy is applied to some extreme and least crowded elitists. The DE strategy updates an elitist based on the differences of the elitists. The personal best positions carry useful information about the Pareto set, and the mutation and DE strategies help MSCLPSO discover the true Pareto front. Experiments conducted on various benchmark problems demonstrate that MSCLPSO can find nondominated solutions distributed reasonably over the true Pareto front in a single run.

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Attributed multi-objective comprehensive learning particle swarm optimization for optimal security of networks

Cited by 29 publications

References 21 publications

A new multi-objective particle swarm optimization algorithm based on decomposition

A new multi-objective particle swarm optimization algorithm based on decomposition

Electric vehicles management enabling G2V and V2G in smart distribution system for maximizing profits using MOMVO

Multiswarm comprehensive learning particle swarm optimization for solving multiobjective optimization problems

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