Imperialist Competitive Algorithm with Independence and Constrained Assimilation

Dzalbs, Ivars; Kalganova, Tatiana; Dear, Ian

doi:10.1109/hora49412.2020.9152916

Cited by 4 publications

(4 citation statements)

References 68 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The Imperialist Competitive Algorithm (ICA) has been also often used for the combinatorial problems. The authors of [21] proposed the ICA with Independence and Constrained Assimilation (ICA-ICA) which combines the classical ICA assimilation and revolution operators, while maintaining population diversity.…”

Section: A Metaheuristic Algorithmsmentioning

confidence: 99%

Adaptive Ant Colony Optimization With Node Clustering for the Multidepot Vehicle Routing Problem

Stodola

Nohel

2023

IEEE Trans. Evol. Computat.

View full text Add to dashboard Cite

This article deals with the novel metaheuristic algorithm based on the Ant Colony Optimization (ACO) principle. It implements several novel mechanisms that improve its overall performance, lower the optimization time, and reduce the negative behavior which is typically connected with ACO-based algorithms (such as prematurely falling into local optima, or the impact of setting of control parameters on the convergence for different problem configurations). The most significant novel techniques, implemented for the first time to solve the Multi-Depot Vehicle Routing Problem (MDVRP), are as follows: (a) node clustering where transition vertices are organized into a set of candidate lists called clusters; (b) adaptive pheromone evaporation which is adapted during optimization according to the diversity of the population of ant solutions (measured by information entropy). Moreover, a new termination condition, based also on the population diversity, is formulated. The effectiveness of the proposed algorithm for the MDVRP is evaluated via a set of experiments on 23 well-known benchmark instances. Performance is compared with several state-of-the-art metaheuristic methods; the results show that the proposed algorithm outperforms these methods in most cases. Furthermore, the novel mechanisms are analyzed and discussed from points of view of performance, optimization time, and convergence. The findings achieved in this article bring new contributions to the very popular ACO-based algorithms; they can be applied to solve not only the MDVRP, but also, if adapted, to related complex NP-hard problems.

show abstract

Section: A Metaheuristic Algorithmsmentioning

confidence: 99%

Adaptive Ant Colony Optimization With Node Clustering for the Multidepot Vehicle Routing Problem

Stodola

Nohel

2023

IEEE Trans. Evol. Computat.

View full text Add to dashboard Cite

show abstract

“…Most of these studies report only the upper bound obtained. However, in [34,35], global optima were obtained for several OR-Lib instances. These values were obtained using the MIP CPLEX library, but consumed significant amounts of processing time to solve large problem instances.…”

Section: Test Scenario For Thirty Mkp Benchmark Problemsmentioning

confidence: 99%

“…In [34], an approach was proposed in which a greedy algorithm is first used to reduce the search space, and then a linear programming algorithm is used to obtain the optimal values of some instances of the OR-Lib. In Dzalbs et al [35], a variant for population selection in a GA using several instances of two benchmark libraries (OR-Lib and Glover and Kochenberg) is presented. Finally, in [36,37], both ACO and PSO are used to solve several SAC-94 instances, achieving the optimal value in all of them.…”

Section: Introductionmentioning

confidence: 99%

Optimization Method to Address Psychosocial Risks through Adaptation of the Multidimensional Knapsack Problem

Eraña-Díaz¹,

Cruz-Chávez²,

Juárez-Pérez

et al. 2021

Mathematics

View full text Add to dashboard Cite

This paper presents a methodological scheme to obtain the maximum benefit in occupational health by attending to psychosocial risk factors in a company. This scheme is based on selecting an optimal subset of psychosocial risk factors, considering the departments’ budget in a company as problem constraints. This methodology can be summarized in three steps: First, psychosocial risk factors in the company are identified and weighted, applying several instruments recommended by business regulations. Next, a mathematical model is built using the identified psychosocial risk factors information and the company budget for risk factors attention. This model represents the psychosocial risk optimization problem as a Multidimensional Knapsack Problem (MKP). Finally, since Multidimensional Knapsack Problem is NP-hard, one simulated annealing algorithm is applied to find a near-optimal subset of factors maximizing the psychosocial risk care level. This subset is according to the budgets assigned for each of the company’s departments. The proposed methodology is detailed using a case of study, and thirty instances of the Multidimensional Knapsack Problem are tested, and the results are interpreted under psychosocial risk problems to evaluate the simulated annealing algorithm’s performance (efficiency and efficacy) in solving these optimization problems. This evaluation shows that the proposed methodology can be used for the attention of psychosocial risk factors in real companies’ cases.

show abstract

“…Figure 1. Randomly distributed sensor nodes perform CB through VAA [6] Optimization is the process of finding the best solution among a set of possible solutions which is applied to maximize profits or minimize cost [7]. There are several optimization methods to solve different kinds of optimization problems.…”

Section: Introductionmentioning

confidence: 99%

Beampattern optimization techniques using metaheuristic algorithm for collaborative beamforming: a review

2023

View full text Add to dashboard Cite

The fast improvements in wireless technology and embedded systems have sparked a renewed interest in collaborative beamforming (CB) approach used in wireless sensor networks (WSNs). Despite the fact that studies on distributed and CB have been conducted for more than 10 years, CB was previously deemed unworkable because of its extreme complexity and difficult-to-attain criteria. It just got well-known in the last several years when cheaply accessible compact wireless communication electronic sensors with high processing capability emerged. These factors contributed as the motivation for this paper's research overview CB in WSNs. We provide the classifications of the static and mobile WSN which is based on the sensor node optimization technique. This paper reviews the metaheuristic algorithms proposed by previous study for beampattern and energy consumption optimization. Finally, this paper also presents a summary analysis of the previous studies in terms of beampattern characteristic and properties, energy consumption and stability.

show abstract

Imperialist Competitive Algorithm with Independence and Constrained Assimilation

Cited by 4 publications

References 68 publications

Adaptive Ant Colony Optimization With Node Clustering for the Multidepot Vehicle Routing Problem

Adaptive Ant Colony Optimization With Node Clustering for the Multidepot Vehicle Routing Problem

Optimization Method to Address Psychosocial Risks through Adaptation of the Multidimensional Knapsack Problem

Beampattern optimization techniques using metaheuristic algorithm for collaborative beamforming: a review

Contact Info

Product

Resources

About