Multi-objective gate assignment based on robustness in hub airports

To enhance the robustness of the gate assignment, reduce the possibility of flight conflict, and improve the quality of passenger services, a multi-objective gate assignment model is proposed by minimizing flight conflict probability and number of flights assigned to aprons. The biogeography-based optimization algorithm is used to solve the proposed model with a new method for estimating the conflict probability. The simulation results show that the ratio of interval time of two flights assigned to the same gate between 60 and 120 min is as high as 82% when the rate of the flights assigned to aprons is controlled below 0.4. This means that the robustness increases greatly, and the probability of flight conflicts decreases, which is beneficial to the implement of flight assignment plan. In addition, the biogeography-based optimization algorithm is more effective to solve the proposed model and very easy to find out the optimal solutions.

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Multi-Objective Whale Optimization Algorithm for Computation Offloading Optimization in Mobile Edge Computing

Sensors ◽

10.3390/s21082628 ◽

2021 ◽

Vol 21 (8) ◽

pp. 2628

Author(s):

Mengxing Huang ◽

Qianhao Zhai ◽

Yinjie Chen ◽

Siling Feng ◽

Feng Shu

Keyword(s):

Optimization Algorithm ◽

Edge Computing ◽

Solution Set ◽

Whale Optimization Algorithm ◽

Computation Offloading ◽

Mobile Edge Computing ◽

Multi Objective ◽

Whale Optimization ◽

Artificial Immune System Algorithm

Computation offloading is one of the most important problems in edge computing. Devices can transmit computation tasks to servers to be executed through computation offloading. However, not all the computation tasks can be offloaded to servers with the limitation of network conditions. Therefore, it is very important to decide quickly how many tasks should be executed on servers and how many should be executed locally. Only computation tasks that are properly offloaded can improve the Quality of Service (QoS). Some existing methods only focus on a single objection, and of the others some have high computational complexity. There still have no method that could balance the targets and complexity for universal application. In this study, a Multi-Objective Whale Optimization Algorithm (MOWOA) based on time and energy consumption is proposed to solve the optimal offloading mechanism of computation offloading in mobile edge computing. It is the first time that MOWOA has been applied in this area. For improving the quality of the solution set, crowding degrees are introduced and all solutions are sorted by crowding degrees. Additionally, an improved MOWOA (MOWOA2) by using the gravity reference point method is proposed to obtain better diversity of the solution set. Compared with some typical approaches, such as the Grid-Based Evolutionary Algorithm (GrEA), Cluster-Gradient-based Artificial Immune System Algorithm (CGbAIS), Non-dominated Sorting Genetic Algorithm III (NSGA-III), etc., the MOWOA2 performs better in terms of the quality of the final solutions.

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Solving Multi-Objective Multicast Routing Problem Using a New Hybrid Approach

International Journal of Applied Evolutionary Computation ◽

10.4018/ijaec.2018100102 ◽

2018 ◽

Vol 9 (4) ◽

pp. 22-36

Author(s):

Mohammed Mahseur ◽

Abdelmadjid Boukra ◽

Yassine Meraihi

Keyword(s):

Multicast Routing ◽

Hybrid Approach ◽

Bat Algorithm ◽

Pareto Optimal Solutions ◽

Optimal Solutions ◽

Routing Problem ◽

Multi Objective ◽

Strength Pareto Evolutionary Algorithm ◽

Quality Of Service Parameters

Multicast routing is the problem of finding the spanning tree of a set of destinations whose roots are the source node and its leaves are the set of destination nodes by optimizing a set of quality of service parameters and satisfying a set of transmission constraints. This article proposes a new hybrid multicast algorithm called Hybrid Multi-objective Multicast Algorithm (HMMA) based on the Strength Pareto Evolutionary Algorithm (SPEA) to evaluate and classify the population in dominated solutions and non-dominated solutions. Dominated solutions are evolved by the Bat Algorithm, and non-dominated solutions are evolved by the Firefly Algorithm. Old and weak solutions are replaced by new random solutions by a process of mutation. The simulation results demonstrate that the proposed algorithm is able to find good Pareto optimal solutions compared to other algorithms.

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Hybrid QPSO-NNIA2 Algorithm for Multi-Objective Optimization Problem

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001419590250 ◽

2019 ◽

Vol 33 (08) ◽

pp. 1959025

Author(s):

Lan Zhang

Keyword(s):

Optimization Algorithm ◽

Optimization Problem ◽

Nearest Neighbor ◽

Superior Performance ◽

Optimal Solutions ◽

Multi Objective Optimization ◽

Nsga Ii ◽

Multi Objective ◽

Evolution Algebra ◽

Neighbor List

To improve the convergence and distribution of a multi-objective optimization algorithm, a hybrid multi-objective optimization algorithm, based on the quantum particle swarm optimization (QPSO) algorithm and adaptive ranks clone and neighbor list-based immune algorithm (NNIA2), is proposed. The contribution of this work is threefold. First, the vicinity distance was used instead of the crowding distance to update the archived optimal solutions in the QPSO algorithm. The archived optimal solutions are updated and maintained by using the dynamic vicinity distance based m-nearest neighbor list in the QPSO algorithm. Secondly, an adaptive dynamic threshold of unfitness function for constraint handling is introduced in the process. It is related to the evolution algebra and the feasible solution. Thirdly, a new metric called the distribution metric is proposed to depict the diversity and distribution of the Pareto optimal. In order to verify the validity and feasibility of the QPSO-NNIA2 algorithm, we compare it with the QPSO, NNIA2, NSGA-II, MOEA/D, and SPEA2 algorithms in solving unconstrained and constrained multi-objective problems. The simulation results show that the QPSO-NNIA2 algorithm achieves superior convergence and superior performance by three metrics compared to other algorithms.

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Research of Biogeography-Based Multi-Objective Evolutionary Algorithm

Journal of Information Technology Research ◽

10.4018/jitr.2011040106 ◽

2011 ◽

Vol 4 (2) ◽

pp. 70-80 ◽

Cited By ~ 4

Author(s):

Hongwei Mo ◽

Zhidan Xu

Keyword(s):

Evolutionary Algorithm ◽

Optimization Algorithm ◽

Solution Set ◽

Test Problems ◽

Benchmark Test ◽

Multi Objective ◽

Simulation Results ◽

Effectiveness And Efficiency ◽

Evolutionary Operators ◽

External Population

Biogeography-based optimization algorithm (BBO) is an optimization algorithm inspired by the migration of animals in nature. A new multi-objective evolutionary algorithm is proposed, which is called Biogeography-based multi-objective evolutionary algorithm (BBMOEA). The fitness assignment and the external population elitism of SPEA2 are adapted to ensure even distribution of the solution set. The population evolutionary operators of BBO are applied to the evolution of the external population to ensure the convergence of the solution set. Simulation results on benchmark test problems illustrate the effectiveness and efficiency of the proposed algorithm.

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Analysis and Modeling of Supply Chain Management of Fresh Products Based on Genetic Algorithm in E-commerce Environment

10.21203/rs.3.rs-73155/v1 ◽

2020 ◽

Author(s):

Yaoting Chen

Keyword(s):

Genetic Algorithm ◽

Supply Chain ◽

Weighted Sum ◽

Pareto Optimal Solutions ◽

Optimal Solutions ◽

Optimal Process ◽

Multi Objective ◽

Multi Objective Genetic Algorithm ◽

Proposed Model ◽

Fresh Products

Abstract BackgroundSupply chain provides the chance to enhance chain performances by decrease these uncertainties. It is a demand for some level of co-ordination of activities and processes within and between organization in the supply chain to decrease uncertainties and increase more cost for customers. Partner selection is an important issue in the supply chain management of fresh products in E-commerce environment. In this paper, we utilized a multi-objective genetic algorithm for evaluation supply chain of fresh products in E-commerce environment. ResultsThe proposed multi-objective genetic algorithm is to search the set of Pareto-optimal solutions for these conflicting objectives using by weighted sum approach. The proposed model suitable for fresh products in E-commerce environment to optimize supply chain are derived. The value of objective 1 (f1) performs approximately nonlinearly with the increasing the value of objective 2,3 and 4 (f2,f3 and f4). At the value of objective 1 of 3.2*105, f2, f3 and f4 is about 4.3*105, 86 and 5.6*104. When the value of objective 1 is increased to 7.6*105, the minimum f2, f3 and f4 is about 3.0*105, 38 and 2.56*104. It is noted that the value of objective 1 is increased from 6.4*105 to 7.6*105, the variation of f2, f3 and f4 is 11.7%, 17.4% and 3.4% respectively. It is pointed out that the variation of f2 and f3 with f1 and f4 is kept within obvious ranges. This practical result highlights the fact that the effects of the fact that effects of f2 and f3 are important factors affecting the performance supply chain network of fresh product in E-commerce environment.ConclusionsIn this paper, we utilized a multi-objective genetic algorithm for evaluation supply chain of fresh products in E-commerce environment. Four objectives for optimal process are included in the proposed model: (1) maximization of green appraisal score, (2) minimization of transportation time and total time comprised of product time, (3) maximization of average product quality, (4) minimization of transportation cost and total cost comprised of product cost. In order to evaluate optimal process, set of Pareto-optimal solutions is obtained based on the weighted sum method.

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An Efficient QoS-aware Services Selection in IoT using A Reputation Improved- Social Spider Optimization Algorithm

10.21203/rs.3.rs-38596/v2 ◽

2020 ◽

Author(s):

Aghabi Nabil Abosaif ◽

Salah Elfaki Elrofai

Keyword(s):

Optimization Algorithm ◽

Likert Scale ◽

End Users ◽

Social Spider ◽

Daily Lives ◽

Social Spider Optimization ◽

Optimal Service ◽

Proposed Model ◽

Spatially Distributed

Abstract Internet of Things (IoT) has been grown rapidly over the last years to connect a considerable number of spatially distributed objects or actuators. The connected objects create new functionality and provide various services to enhance and satisfy End-users daily lives. The issue is to provide the End-users with optimal services based on their requirements. The critical challenge is to select the optimal service from similar services functionally and various services non-functionality requirements (Quality of services). To achieve this challenge, this paper proposed a services selection model under QoS constraints in the IoT environment. The introduced model implements a meta-heuristic optimization algorithm with a friendly Likert scale measurement method. It aims to improve the performance of bio-inspired optimizing algorithms, called a Social Spider Optimization (SSO) Algorithm, by adding a reputation value to member's weight. The proposed model used a Likert scale measurement to evaluate the reputation of the services from the End-users. In the experiments, a comparative study was done between an original SSO and the proposed RI-SSO model. The results show the efficiency of the proposed RI-SSO model against the original SSO, in both maximization and minimization problems. It obtains a better outperform in terms of fitness values.

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A multi-objective multi-agent optimization algorithm for the multi-skill resource-constrained project scheduling problem with transfer times

RAIRO - Operations Research ◽

10.1051/ro/2021087 ◽

2021 ◽

Author(s):

Amirhossein Hosseinian ◽

Vahid Baradaran

Keyword(s):

Optimization Algorithm ◽

Project Scheduling ◽

Scheduling Problem ◽

Resource Constrained ◽

Resource Constrained Project Scheduling ◽

Multi Objective ◽

Proposed Model ◽

Multi Agent ◽

Project Scheduling Problem ◽

Transfer Times

This paper addresses the Multi-Skill Resource-Constrained Project Scheduling Problem with Transfer Times (MSRCPSP-TT). A new model has been developed that incorporates the presence of transfer times within the multi-skill RCPSP. The proposed model aims to minimize project’s duration and cost, concurrently. The MSRCPSP-TT is an NP-hard problem; therefore, a Multi-Objective Multi-Agent Optimization Algorithm (MOMAOA) is proposed to acquire feasible schedules. In the proposed algorithm, each agent represents a feasible solution that works with other agents in a grouped environment. The agents evolve due to their social, autonomous, and self-learning behaviors. Moreover, the adjustment of environment helps the evolution of agents as well. Since the MSRCPSP-TT is a multi-objective optimization problem, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is used in different procedures of the MOMAOA. Another novelty of this paper is the application of TOPSIS in different procedures of the MOMAOA. These procedures are utilized for: (1) detecting the leader agent in each group, (2) detecting the global best leader agent, and (3) the global social behavior of the MOMAOA. The performance of the MOMAOA has been analyzed by solving several benchmark problems. The results of the MOMAOA have been validated through comparisons with three other meta-heuristics. The parameters of algorithms are determined by the Response Surface Methodology (RSM). The Kruskal-Wallis test is implemented to statistically analyze the efficiency of methods. Computational results reveal that the MOMAOA can beat the other three methods according to several testing metrics. Furthermore, the impact of transfer times on project’s duration and cost has been assessed. The investigations indicate that resource transfer times have significant impact on both objectives of the proposed model

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Multi-objective optimization model for airport gate assignment problem

Aircraft Engineering and Aerospace Technology ◽

10.1108/aeat-09-2020-0217 ◽

2021 ◽

Vol 93 (2) ◽

pp. 311-318

Author(s):

Ramazan Kursat Cecen

Keyword(s):

Assignment Problem ◽

Air Traffic ◽

Walking Distance ◽

Content Type ◽

Multi Objective ◽

Air Traffic Controllers ◽

Proposed Model ◽

Milp Model ◽

Gate Assignment ◽

Short Time

Purpose The purpose of this paper is to provide feasible and fast solutions for the multi-objective airport gate assignment problem (AGAP) considering both passenger-oriented and airline-oriented objectives, which is the total walking distance from gate to baggage carousels (TWD) and the total aircraft fuel consumption during taxi operations (TFC). In addition, obtaining feasible and near-optimal solutions in a short time reduces the gate planning time to be spent by air traffic controllers. Design/methodology/approach The mixed integer linear programming (MILP) approach is implemented to solve the multi-objective AGAP. The weighted sum approach technique was applied in the model to obtain non-dominated solutions. Because of the complexity of the problem, the simulated annealing (SA) algorithm was used for the proposed model. The results were compared with baseline results, which were obtained from the algorithm using the fastest gate assignment and baggage carousel combinations without any conflict taking place at the gate assignments. Findings The proposed model noticeably decreased both the TWD and TFC. The improvement of the TWD and TFC changed from 22.8% to 46.9% and from 4.7% to 7.1%, respectively, according to the priorities of the objectives. Additionally, the average number of non-dominated solutions was calculated as 6.94, which presents many feasible solutions for air traffic controllers to manage ground traffic while taking the airline and passenger objectives into consideration. Practical implications The proposed MILP model includes the objectives of different stakeholders: air traffic controllers, passengers and airlines. In addition, the proposed model can provide feasible gate and baggage carousel assignments together in a short time. Therefore, the model creates a flexibility for air traffic controllers to re-arrange assignments if any unexpected situations take place. Originality/value The proposed MILP model combines the TWD and TFC together for the AGAP problem using the SA. Moreover, the proposed model integrates passenger-oriented and airline-oriented objectives together and reveals the relationships between the objectives in only a short time.

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Research of Biogeography-Based Multi-Objective Evolutionary Algorithm

Interdisciplinary Advances in Information Technology Research ◽

10.4018/978-1-4666-3625-5.ch010 ◽

2013 ◽

pp. 125-135 ◽

Cited By ~ 1

Author(s):

Hongwei Mo ◽

Zhidan Xu

Keyword(s):

Evolutionary Algorithm ◽

Optimization Algorithm ◽

Solution Set ◽

Test Problems ◽

Benchmark Test ◽

Multi Objective ◽

Simulation Results ◽

Effectiveness And Efficiency ◽

Evolutionary Operators ◽

External Population

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Multi-Objective Fixed-Charge Transportation Problem with Random Rough Variables

International Journal of Uncertainty Fuzziness and Knowledge-Based Systems ◽

10.1142/s0218488518500435 ◽

2018 ◽

Vol 26 (06) ◽

pp. 971-996 ◽

Cited By ~ 13

Author(s):

Sankar Kumar Roy ◽

Sudipta Midya ◽

Vincent F. Yu

Keyword(s):

Transportation Problem ◽

Deterministic Model ◽

Real Life ◽

Optimal Solution ◽

Fuzzy Programming ◽

Fixed Charge ◽

Optimal Solutions ◽

Multi Objective ◽

Proposed Model ◽

Fixed Charge Transportation Problem

This paper considers a multi-objective fixed-charge transportation problem (MOFCTP) in which the parameters of the objective functions are random rough variables, while the supply and the demand parameters are rough variables. In real-life situations, the parameters of a multi-objective fixed-charge transportation problem may not be defined precisely, because of globalization of the market, uncontrollable factors, etc. As such, the multi-objective fixed-charge transportation problem is proposed under rough and random rough environments. To tackle uncertain (rough and random rough) parameters, the proposed model employs an expected value operator. Furthermore, a procedure is developed for converting the uncertain multi-objective fixed-charge transportation problem into a deterministic form and then solving the deterministic model. Three different methods, namely, the fuzzy programming, global criterion, and ϵ-constrained methods, are used to derive the optimal compromise solutions of the suggested model. To provide the preferable optimal solution of the formulated problem, a comparison is drawn among the optimal solutions that are extracted from different methods. Herein, the ϵ-constrained method derives a set of optimal solutions and generates an exact Paretofront. Finally, in order to show the applicability and feasibility of the proposed model, the paper includes a real-life example of a multi-objective fixed-charge transportation problem. The main contribution of the paper is that it deals with MOFCTP using two types of uncertainties, thus making the decision making process more flexible.

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