An evolutionary method for community detection using a novel local search strategy

2019 ◽  
Vol 523 ◽  
pp. 457-475 ◽  
Author(s):  
Mehdi Moradi ◽  
Saeed Parsa
Keyword(s):  
Local Search ◽  
Community Detection ◽  
Search Strategy ◽  
Evolutionary Method ◽  
Local Search Strategy

scholarly journals Optimization Algorithms for Detection of Social Interactions

Algorithms ◽  
2020 ◽  
Vol 13 (6) ◽  
pp. 139 ◽  
Author(s):  
Vincenzo Cutello ◽  
Georgia Fargetta ◽  
Mario Pavone ◽  
Rocco A. Scollo
Keyword(s):  
Social Networks ◽  
Local Search ◽  
Social Interactions ◽  
Community Detection ◽  
Experimental Analysis ◽  
Search Strategy ◽  
Random Search ◽  
Optimization Methods ◽  
Research Areas ◽  
Greedy Optimization

Community detection is one of the most challenging and interesting problems in many research areas. Being able to detect highly linked communities in a network can lead to many benefits, such as understanding relationships between entities or interactions between biological genes, for instance. Two different immunological algorithms have been designed for this problem, called Opt-IA and Hybrid-IA, respectively. The main difference between the two algorithms is the search strategy and related immunological operators developed: the first carries out a random search together with purely stochastic operators; the last one is instead based on a deterministic Local Search that tries to refine and improve the current solutions discovered. The robustness of Opt-IA and Hybrid-IA has been assessed on several real social networks. These same networks have also been considered for comparing both algorithms with other seven different metaheuristics and the well-known greedy optimization Louvain algorithm. The experimental analysis conducted proves that Opt-IA and Hybrid-IA are reliable optimization methods for community detection, outperforming all compared algorithms.

scholarly journals A Local Search-Based Generalized Normal Distribution Algorithm for Permutation Flow Shop Scheduling

2021 ◽  
Vol 11 (11) ◽  
pp. 4837
Author(s):  
Mohamed Abdel-Basset ◽  
Reda Mohamed ◽  
Mohamed Abouhawwash ◽  
Victor Chang ◽  
S. S. Askar
Keyword(s):  
Local Search ◽  
Search Strategy ◽  
Flow Shop ◽  
Flow Shop Scheduling ◽  
Discrete Problem ◽  
Mutation Operator ◽  
Shop Scheduling ◽  
Permutation Flow Shop Scheduling ◽  
Local Search Strategy ◽  
Generalized Normal Distribution

This paper studies the generalized normal distribution algorithm (GNDO) performance for tackling the permutation flow shop scheduling problem (PFSSP). Because PFSSP is a discrete problem and GNDO generates continuous values, the largest ranked value rule is used to convert those continuous values into discrete ones to make GNDO applicable for solving this discrete problem. Additionally, the discrete GNDO is effectively integrated with a local search strategy to improve the quality of the best-so-far solution in an abbreviated version of HGNDO. More than that, a new improvement using the swap mutation operator applied on the best-so-far solution to avoid being stuck into local optima by accelerating the convergence speed is effectively applied to HGNDO to propose a new version, namely a hybrid-improved GNDO (HIGNDO). Last but not least, the local search strategy is improved using the scramble mutation operator to utilize each trial as ideally as possible for reaching better outcomes. This improved local search strategy is integrated with IGNDO to produce a new strong algorithm abbreviated as IHGNDO. Those proposed algorithms are extensively compared with a number of well-established optimization algorithms using various statistical analyses to estimate the optimal makespan for 41 well-known instances in a reasonable time. The findings show the benefits and speedup of both IHGNDO and HIGNDO over all the compared algorithms, in addition to HGNDO.

scholarly journals Noninferior Solution Grey Wolf Optimizer with an Independent Local Search Mechanism for Solving Economic Load Dispatch Problems

Energies ◽  
2019 ◽  
Vol 12 (12) ◽  
pp. 2274 ◽  
Author(s):  
Jianzhong Xu ◽  
Fu Yan ◽  
Kumchol Yun ◽  
Lifei Su ◽  
Fengshu Li ◽  
...  
Keyword(s):  
Power Systems ◽  
Local Search ◽  
Optimization Problem ◽  
Search Strategy ◽  
Local Optimum ◽  
Economic Load Dispatch ◽  
Problem Solution ◽  
Main Task ◽  
Grey Wolf ◽  
Local Search Strategy

The economic load dispatch (ELD) problem is a complex optimization problem in power systems. The main task for this optimization problem is to minimize the total fuel cost of generators while also meeting the conditional constraints of valve-point loading effects, prohibited operating zones, and nonsmooth cost functions. In this paper, a novel grey wolf optimization (GWO), abbreviated as NGWO, is proposed to solve the ELD problem by introducing an independent local search strategy and a noninferior solution neighborhood independent local search technique to the original GWO algorithm to achieve the best problem solution. A local search strategy is added to the standard GWO algorithm in the NGWO, which is called GWOI, to search the local neighborhood of the global optimal point in depth and to guarantee a better candidate. In addition, a noninferior solution neighborhood independent local search method is introduced into the GWOI algorithm to find a better solution in the noninferior solution neighborhood and ensure the high probability of jumping out of the local optimum. The feasibility of the proposed NGWO method is verified on five different power systems, and it is compared with other selected methods in terms of the solution quality, convergence rate, and robustness. The compared experimental results indicate that the proposed NGWO method can efficiently solve ELD problems with higher-quality solutions.

A hybrid multi-objective PSO algorithm with local search strategy for VLSI partitioning

2014 ◽  
Vol 8 (2) ◽  
pp. 203-216 ◽  
Author(s):  
Wenzhong Guo ◽  
Genggeng Liu ◽  
Guolong Chen ◽  
Shaojun Peng
Keyword(s):  
Local Search ◽  
Search Strategy ◽  
Pso Algorithm ◽  
Multi Objective ◽  
Local Search Strategy
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