Metaheuristics for dynamic combinatorial optimization problems

Yang, Shengxiang; Jiang, Ya Dong; Nguyễn, Trung Thành

doi:10.1093/imaman/dps021

Cited by 50 publications

(26 citation statements)

References 105 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Formally, a DOP can be defined as follows: DOP = optimize f (x, t) subject to X(t) ⊆ S , t ∈ T, (1) where S is the search space, t is the time, f : S × T → R is the objective function that assigns a value (i.e., R) to each possible solution x ∈ S and X(t) is the set of feasible solutions x ∈ X(t) ⊆ S at time t [13,15]. Each feasible solution x consists of optimization variables x = {x 1 , .…”

Section: Dynamic Optimization Problem (Dop)mentioning

confidence: 99%

“…However, it has been a growing interest to apply SI algorithms on different DOPs. EDO has received extensive attention with several surveys [13,12,14,15] and books [16,17,18,19,20], whereas SI dynamic optimization (SIDO) has not received much attention, with exception of some very brief reviews of PSO in [14] and ACO in [15] included as subsections in the EDO surveys. The aim of this paper is to extend these reviews of ACO and PSO and provide a comprehensive survey of existing work done related to SIDO, which also includes the less popular and recent SI algorithms.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A survey of swarm intelligence for dynamic optimization: Algorithms and applications

Mavrovouniotis

Yang

2017

Swarm and Evolutionary Computation

472

213

View full text Add to dashboard Cite

Swarm intelligence (SI) algorithms, including ant colony optimization, particle swarm optimization, bee-inspired algorithms, bacterial foraging optimization, firefly algorithms, fish swarm optimization and many more, have been proven to be good methods to address difficult optimization problems under stationary environments. Most SI algorithms have been developed to address stationary optimization problems and hence, they can converge on the (near-) optimum solution efficiently. However, many real-world problems have a dynamic environment that changes over time. For such dynamic optimization problems (DOPs), it is difficult for a conventional SI algorithm to track the changing optimum once the algorithm has converged on a solution. In the last two decades, there has been a growing interest of addressing DOPs using SI algorithms due to their adaptation capabilities. This paper presents a broad review on SI dynamic optimization (SIDO) focused on several classes of problems, such as discrete, continuous, constrained, multi-objective and classification problems, and real-world applications. In addition, this paper focuses on the enhancement strategies integrated in SI algorithms to address dynamic changes, the performance measurements and benchmark generators used in SIDO. Finally, some considerations about future directions in the subject are given.

show abstract

Section: Dynamic Optimization Problem (Dop)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A survey of swarm intelligence for dynamic optimization: Algorithms and applications

Mavrovouniotis

Yang

2017

Swarm and Evolutionary Computation

472

213

View full text Add to dashboard Cite

show abstract

“…The methods in which immigrant schemes like random immigrants and elitism based immigrants were hybridized in different proportions and incorporated to ACO were considered as ACO with hybrid immigrant schemes (HIACO) [32].…”

Section: A Ant Colony Optimization With Hybrid Immigrant Schemesmentioning

confidence: 99%

Dynamic Vehicle Routing Problem: Solution by Ant Colony Optimization with Hybrid Immigrant Schemes

K.M.¹,

Kanmani²

2017

IJISA

View full text Add to dashboard Cite

Abstract-During past decades, several Meta-Heuristics were considered by researchers to solve Dynamic Vehicle Routing Problem.In this paper, Ant Colony Optimization integrated with Hybrid Immigrant Schemes methods are proposed for solving Dynamic Vehicle Routing Problem. Ant Colony Optimization with hybrid immigrant schemes methods namely HIACO-I, HIACO-II and HIACO-III focused on establishing the proper balance between intensification and diversification. The performance evaluation of the algorithms in which Random Immigrants and Elitism based Immigrants were hybridized in different proportions and added to Ant Colony Optimization algorithm showed that they had produced better results in many dynamic test cases generated from three Vehicle Routing Problem instances.

show abstract

“…Shah and Reed (2011);Martins et al (2014)) and dynamic environments (e.g. Yang et al (2013) with different modifications. The dynamic property of the knapsack problem is achieved when the problem parameters (such as item weight, value and sack capacity) are time dependent and subject to variation.…”

Section: Dynamic Knapsack Problemmentioning

confidence: 99%

Adaptive-mutation compact genetic algorithm for dynamic environments

et al. 2016

View full text Add to dashboard Cite

In recent years, the interest in studying nature inspired optimization algorithms for dynamic optimization problems (DOPs) has been increasing constantly due to its importance in real-world applications. Several techniques such as hyper-selection, change prediction, hypermutation and many more have been developed to address DOPs. Among these techniques, the hypermutation scheme has proved beneficial for addressing DOPs but requires that the mutation factors be picked a priori and this is one of the limitation of the hypermutation scheme.This paper investigates variants of the recently proposed adaptive-mutation compact genetic algorithm (amcGA). The amcGA is made up of a change detection scheme and mutation schemes, where the degree of change regulates the probability of mutation (i.e. the probability of mutation is directly proportional to the degree of change). This paper also presents a change trend scheme for the amcGA so as to boost its performance whenever a change occurs. Experimental results shows that the change trend and mutation schemes has an

show abstract

Metaheuristics for dynamic combinatorial optimization problems

Cited by 50 publications

References 105 publications

A survey of swarm intelligence for dynamic optimization: Algorithms and applications

A survey of swarm intelligence for dynamic optimization: Algorithms and applications

Dynamic Vehicle Routing Problem: Solution by Ant Colony Optimization with Hybrid Immigrant Schemes

Adaptive-mutation compact genetic algorithm for dynamic environments

Contact Info

Product

Resources

About