scholarly journals Enhancing the Performance of Biogeography-Based Optimization Using Multitopology and Quantitative Orthogonal Learning

2017 ◽  
Vol 2017 ◽  
pp. 1-23 ◽  
Author(s):  
Siao Wen ◽  
Jinfu Chen ◽  
Yinhong Li ◽  
Dongyuan Shi ◽  
Xianzhong Duan
Keyword(s):  
Orthogonal Design ◽  
The Other ◽  
Feasible Region ◽  
Migration Process ◽  
Migration Strategy ◽  
Solution Quality ◽  
Migration Operator ◽  
Global Topology ◽  
Convergence Performance ◽  
Orthogonal Learning

Two defects of biogeography-based optimization (BBO) are found out by analyzing the characteristics of its dominant migration operator. One is that, due to global topology and direct-copying migration strategy, information in several good-quality habitats tends to be copied to the whole habitats rapidly, which would lead to premature convergence. The other is that the generated solutions by migration process are distributed only in some specific regions so that many other areas where competitive solutions may exist cannot be investigated. To remedy the former, a new migration operator precisely developed by modifying topology and copy mode is introduced to BBO. Additionally, diversity mechanism is proposed. To remedy the latter defect, quantitative orthogonal learning process accomplished based on space quantizing and orthogonal design is proposed. It aims to investigate the feasible region thoroughly so that more competitive solutions can be obtained. The effectiveness of the proposed approaches is verified on a set of benchmark functions with diverse characteristics. The experimental results reveal that the proposed method has merits regarding solution quality, convergence performance, and so on, compared with basic BBO, five BBO variant algorithms, seven orthogonal learning-based algorithms, and other non-OL-based evolutionary algorithms. The effects of each improved component are also analyzed.

scholarly journals Optimal Scheduling of a Microgrid Using Multiobjective Biogeography-Based Optimization Model and Algorithm with Adaptive Migration

2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Bo Li ◽  
Jue Wang ◽  
Nan Xia
Keyword(s):  
Pareto Frontier ◽  
Evolutionary Model ◽  
Optimal Scheduling ◽  
Constraint Handling ◽  
Migration Strategy ◽  
Suitability Index ◽  
Proposed Model ◽  
Convergence Performance ◽  
Distribution Uniformity ◽  
And Migration

Aiming at the important research topic of optimal scheduling in the microgrid field, the general model for multiobjective dynamic optimal scheduling of a microgrid is established with the objective of minimizing economic and environmental costs. On this basis, the model is organically integrated with constraint handling technology, multiobjective optimization, and biogeography-based optimization algorithm, and then a constrained multiobjective evolutionary model suitable for biogeography-based optimization is further established. The corresponding constraint handling mechanism, the determination method of habitat suitability index, and migration strategy are improved, and the convergence performance and the distribution uniformity of Pareto frontier for multiobjective evolutionary algorithm are effectively enhanced. Applied to the optimal scheduling of typical microgrid systems, the effectiveness of the proposed model and method is verified.

AN INCREMENTAL OBJECT-ORIENTED MIGRATION STRATEGY FOR RPG LEGACY SYSTEMS

1999 ◽  
Vol 09 (01) ◽  
pp. 5-25 ◽  
Author(s):  
GERARDO CANFORA ◽  
ANDREA DE LUCIA ◽  
GIUSEPPE A. DI LUCCA
Keyword(s):  
Reverse Engineering ◽  
Object Oriented ◽  
Legacy Systems ◽  
Application Domain ◽  
Migration Process ◽  
Migration Strategy ◽  
Design Metrics ◽  
Object Oriented Design ◽  
The Core ◽  
Persistent Data

We present a strategy for incrementally migrating legacy systems to object-oriented platforms. The migration process consists of six sequential phases and encompasses reverse engineering and reengineering activities. The aim of reverse engineering is to decompose programs into components implementing the user interface and components implementing application domain objects. The identification of objects is centred around persistent data stores and exploits object-oriented design metrics. Wrapping is the core of the reengineering activities. It makes new systems able to exploit existing resources, thus allowing an incremental and selective replacement of the identified objects. The migration strategy has been defined and experimented within the project ERCOLE (Encapsulation, Reengineering and Coexistence of Object with Legacy) on legacy systems developed in RPG for the IBM AS/400 environment.

How Does it Feel to Be Part of the Minority?: Impacts of Perspective Taking on Prosocial Behavio

2021 ◽  
Author(s):  
Marisol Rodríguez Chatruc ◽  
Sandra V. Rozo
Keyword(s):  
Vulnerable Populations ◽  
Perspective Taking ◽  
The Other ◽  
Prosocial Behaviors ◽  
Online Game ◽  
Migration Process ◽  
Life Decisions

Can online experiences that illustrate the lives of vulnerable populations improve prosocial behaviors and reduce prejudice? We randomly assign 850 individuals to: i) an online game that immerses individuals in the life decisions of a Venezuelan migrant and ii) a documentary about the migration process of Venezuelans to Colombia. Both treatments effectively improve altruism and reduce prejudice towards migrants. The impacts of both treatments are not statistically different in any of the other outcomes that we examine. The effects of the game are mainly driven by changes in perspective-taking while the effects of the video are induced by changes in both empathy and perspective-taking.

Heuristic approaches for operational aircraft maintenance routing problem with maximum flying hours and man-power availability considerations

2017 ◽  
Vol 117 (10) ◽  
pp. 2142-2170 ◽  
Author(s):  
Abdelrahman E.E. Eltoukhy ◽  
Felix T.S. Chan ◽  
S.H. Chung ◽  
Ben Niu ◽  
X.P. Wang
Keyword(s):  
Airline Industry ◽  
Solution Algorithm ◽  
The Other ◽  
Computational Time ◽  
Aircraft Maintenance ◽  
Solution Quality ◽  
Routing Problem ◽  
Content Type ◽  
Other Hand ◽  
Availability Constraints

Purpose The purpose of this paper is twofold. First, to propose an operational model for aircraft maintenance routing problem (AMRP) rather than tactical models that are commonly used in the literature. Second, to develop a fast and responsive solution method in order to cope with the frequent changes experienced in the airline industry. Design/methodology/approach Two important operational considerations were considered, simultaneously. First one is the maximum flying hours, and second one is the man-power availability. On the other hand, ant colony optimization (ACO), simulated annealing (SA), and genetic algorithm (GA) approaches were proposed to solve the model, and the upper bound was calculated to be the criteria to assess the performance of each meta-heuristic. After attempting to solve the model by these meta-heuristics, the authors noticed further improvement chances in terms of solution quality and computational time. Therefore, a new solution algorithm was proposed, and its performance was validated based on 12 real data from the EgyptAir carrier. Also, the model and experiments were extended to test the effect of the operational considerations on the profit. Findings The computational results showed that the proposed solution algorithm outperforms other meta-heuristics in finding a better solution in much less time, whereas the operational considerations improve the profitability of the existing model. Research limitations/implications The authors focused on some operational considerations rather than tactical considerations that are commonly used in the literature. One advantage of this is that it improves the profitability of the existing models. On the other hand, identifying future research opportunities should help academic researchers to develop new models and improve the performance of the existing models. Practical implications The experiment results showed that the proposed model and solution methods are scalable and can thus be adopted by the airline industry at large. Originality/value In the literature, AMRP models were cast with approximated assumption regarding the maintenance issue, while neglecting the man-power availability consideration. However, in this paper, the authors attempted to relax that maintenance assumption, and consider the man-power availability constraints. Since the result showed that these considerations improve the profitability by 5.63 percent in the largest case. The proposed operational considerations are hence significant. Also, the authors utilized ACO, SA, and GA to solve the model for the first time, and developed a new solution algorithm. The value and significance of the new algorithm appeared as follow. First, the solution quality was improved since the average improvement ratio over ACO, SA, and GA goes up to 8.30, 4.45, and 4.00 percent, respectively. Second, the computational time was significantly improved since it does not go beyond 3 seconds in all the 12 real cases, which is considered much lesser compared to ACO, SA, and GA.

scholarly journals Chaotic Jaya Approaches to Solving Electromagnetic Optimization Benchmark Problems

Telecom ◽  
2021 ◽  
Vol 2 (2) ◽  
pp. 222-231
Author(s):  
Leandro dos S. Coelho ◽  
Viviana C. Mariani ◽  
Sotirios K. Goudos ◽  
Achilles D. Boursianis ◽  
Konstantinos Kokkinidis ◽  
...  
Keyword(s):  
Optimization Problems ◽  
Population Based ◽  
Benchmark Problems ◽  
Jaya Algorithm ◽  
Solution Quality ◽  
Chaotic Mapping ◽  
Logistic Mapping ◽  
Essential Idea ◽  
Convergence Performance ◽  
Application Fields

The Jaya optimization algorithm is a simple, fast, robust, and powerful population-based stochastic metaheuristic that in recent years has been successfully applied in a variety of global optimization problems in various application fields. The essential idea of the Jaya algorithm is that the searching agents try to change their positions toward the best obtained solution by avoiding the worst solution at every generation. The important difference between Jaya and other metaheuristics is that Jaya does not require the tuning of its control, except for the maximum number of iterations and population size parameters. However, like other metaheuristics, Jaya still has the dilemma of an appropriate tradeoff between its exploration and exploitation abilities during the evolution process. To enhance the convergence performance of the standard Jaya algorithm in the continuous domain, chaotic Jaya (CJ) frameworks based on chaotic sequences are proposed in this paper. In order to obtain the performance of the standard Jaya and CJ approaches, tests related to electromagnetic optimization using two different benchmark problems are conducted. These are the Loney’s solenoid benchmark and a brushless direct current (DC) motor benchmark. Both problems are realized to evaluate the effectiveness and convergence rate. The simulation results and comparisons with the standard Jaya algorithm demonstrated that the performance of the CJ approaches based on Chebyshev-type chaotic mapping and logistic mapping can be competitive results in terms of both efficiency and solution quality in electromagnetics optimization.

The Other Side of the Paradox: The Risk of Low Birth Weight among Infants of Migrant and Nonmigrant Households within Mexico

2002 ◽  
Vol 36 (3) ◽  
pp. 746-765 ◽  
Author(s):  
Reanne Frank ◽  
Robert A. Hummer
Keyword(s):  
Birth Weight ◽  
Low Birth Weight ◽  
International Migration ◽  
Perinatal Outcomes ◽  
Total Sample ◽  
The Other ◽  
Representative Survey ◽  
Migration Process ◽  
Migrant Household ◽  
Nationally Representative

The main aim of this study is to understand how the international migration process affects the risk of low birth weight among Mexican-born infants using the ENADID 1997 (Encuesta Nacional de la Dinámica Demográfica), a nationally representative survey of the Mexican population. The total sample includes 23,607 infants. We employ logistic regression to estimate models in which migration status is included as a risk factor for low birth weight. The analysis demonstrates that membership in a migrant household provides protection from the risk of low birth weight largely through the receipt of remittances. In light of this evidence, it is particularly important that international migration be recognized as one of the processes that has a positive and significant effect on perinatal outcomes in both countries of origin and in countries of destination.

Parameter optimization of power system stabilizers via kidney-inspired algorithm

2018 ◽  
Vol 41 (5) ◽  
pp. 1405-1417 ◽  
Author(s):  
Serdar Ekinci ◽  
Aysen Demiroren ◽  
Baran Hekimoglu
Keyword(s):  
Power System ◽  
Damping Ratio ◽  
Computation Time ◽  
Bat Algorithm ◽  
Population Based ◽  
The Other ◽  
Superior Performance ◽  
Power System Stabilizers ◽  
Solution Quality ◽  
Nonlinear Simulation

This article describes the application of a new population-based meta-heuristic optimization algorithm inspired by the kidney process in the human body for the tuning of power system stabilizers (PSSs) in a multi-machine power system. The tuning problem of PSS parameters is formulated as an optimization problem that aims at maximizing the damping ratio of the electromechanical modes and the kidney-inspired algorithm (KA) is used to search for the optimal parameters. The efficacy of the KA-based PSS design was successfully tested on a well-known 16-machine, 68-bus power system. The obtained results are evaluated and compared with the other results obtained by the original particle swarm optimization (PSO) and the bat algorithm (BA) methods. From the detailed eigenvalue analysis, the nonlinear simulation studies and some performance indices it has been found out that for damping oscillations, the performance of the proposed KA approach in this study is better than that obtained by other intelligent techniques (PSO and BA). Moreover, the efficiency and the superior performance of the proposed method over the other two algorithms in terms of computation time, convergence rate and solution quality are confirmed.

scholarly journals Decentralized Algorithms for Weapon-Target Assignment in Swarming Combat System

2019 ◽  
Vol 2019 ◽  
pp. 1-15
Author(s):  
Peng Zhao ◽  
Jianzhong Wang ◽  
Lingren Kong
Keyword(s):  
Hybrid Algorithm ◽  
The Other ◽  
Damage Effect ◽  
Ground Vehicles ◽  
Solution Quality ◽  
Decentralized Algorithms ◽  
Average Improvement ◽  
Optimal Value ◽  
Target Assignment ◽  
First Time

Swarming small unmanned aerial or ground vehicles (UAVs or UGVs) have attracted the attention of worldwide military powers as weapons, and the weapon-target assignment (WTA) problem is extremely significant for swarming combat. The problem involves assigning weapons to targets in a decentralized manner such that the total damage effect of targets is maximized while considering the nonlinear cumulative damage effect. Two improved optimization algorithms are presented in the study. One is the redesigned auction-based algorithm in which the bidding rules are properly modified such that the auction-based algorithm is applied for the first time to solve a nonlinear WTA problem. The other one is the improved task swap algorithm that eliminates the restriction in which the weights of the edges on graph G must be positive. Computational results for up to 120 weapons and 110 targets indicate that the redesigned auction-based algorithm yields an average improvement of 37% over the conventional auction-based algorithm in terms of solution quality while the additional running time is negligible. The improved task swap algorithm and the other two popular task swap algorithms almost achieve the same optimal value, while the average time-savings of the proposed algorithm correspond to 53% and 74% when compared to the other two popular task swap algorithms. Furthermore, the hybrid algorithm that combines the above two improved algorithms is examined. Simulations indicate that the hybrid algorithm exhibits superiority in terms of solution quality and time consumption over separately implementing the aforementioned two improved algorithms.

scholarly journals Second-Order Learning Algorithm with Squared Penalty Term

2000 ◽  
Vol 12 (3) ◽  
pp. 709-729 ◽  
Author(s):  
Kazumi Saito ◽  
Ryohei Nakano
Keyword(s):  
Supervised Learning ◽  
Cross Validation ◽  
Learning Algorithm ◽  
Second Order ◽  
The Other ◽  
Penalty Term ◽  
First Order ◽  
Penalty Factor ◽  
Convergence Performance ◽  
On Line

This article compares three penalty terms with respect to the efficiency of supervised learning, by using first- and second-order off-line learning algorithms and a first-order on-line algorithm. Our experiments showed that for a reasonably adequate penalty factor, the combination of the squared penalty term and the second-order learning algorithm drastically improves the convergence performance in comparison to the other combinations, at the same time bringing about excellent generalization performance. Moreover, in order to understand how differently each penalty term works, a function surface evaluation is described. Finally, we show how cross validation can be applied to find an optimal penalty factor.

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