Multi-Layer Distributed Constraint Satisfaction for Multi-criteria Optimization Problem

2020 ◽  
Vol 11 (2) ◽  
pp. 134-155 ◽  
Author(s):  
Mouna Gargouri Mnif ◽  
Sadok Bouamama
Keyword(s):  
Constraint Satisfaction ◽  
Optimization Problem ◽  
Transportation Network ◽  
Optimization Methods ◽  
Planning Problem ◽  
Scheduling Problems ◽  
Present Contribution ◽  
Multimodal Transportation ◽  
New Approach ◽  
Distributed Constraint Satisfaction

This article introduces a new approach to solve the multimodal transportation network planning problem (MTNP). In this problem, the commodities must be transported from an international network by at least two different transport modes. The main purpose is to identify the best multimodal transportation strategy. The present contribution focuses on efficient optimization methods to solve MTNP. This includes the assignment and the scheduling problems. The authors split the MTNP into layered. Each layer is presented by an agent. These agents interact, collaborate, and communicate together to solve the problem. This article defines MTNP as a distributed constraint satisfaction multi-criteria optimization problem (DCSMOP). This latter is a description of the constraint optimization problem (COP), where variables and constraints are distributed among a set of agents. Each agent can interact with other agents to share constraints and to distribute complementary tasks. Experimental results are the proof of this work efficiently.

scholarly journals A Bat Algorithm with Mutation for UCAV Path Planning

2012 ◽  
Vol 2012 ◽  
pp. 1-15 ◽  
Author(s):  
Gaige Wang ◽  
Lihong Guo ◽  
Hong Duan ◽  
Luo Liu ◽  
Heqi Wang
Keyword(s):  
Path Planning ◽  
Optimization Problem ◽  
Bat Algorithm ◽  
Optimization Methods ◽  
Population Based ◽  
Planning Problem ◽  
New Approach ◽  
Air Vehicle ◽  
Flight Route ◽  
Path Planning Problem

Path planning for uninhabited combat air vehicle (UCAV) is a complicated high dimension optimization problem, which mainly centralizes on optimizing the flight route considering the different kinds of constrains under complicated battle field environments. Original bat algorithm (BA) is used to solve the UCAV path planning problem. Furthermore, a new bat algorithm with mutation (BAM) is proposed to solve the UCAV path planning problem, and a modification is applied to mutate between bats during the process of the new solutions updating. Then, the UCAV can find the safe path by connecting the chosen nodes of the coordinates while avoiding the threat areas and costing minimum fuel. This new approach can accelerate the global convergence speed while preserving the strong robustness of the basic BA. The realization procedure for original BA and this improved metaheuristic approach BAM is also presented. To prove the performance of this proposed metaheuristic method, BAM is compared with BA and other population-based optimization methods, such as ACO, BBO, DE, ES, GA, PBIL, PSO, and SGA. The experiment shows that the proposed approach is more effective and feasible in UCAV path planning than the other models.

scholarly journals Multimodal Processes Rescheduling: Cyclic Steady States Space Approach

2013 ◽  
Vol 2013 ◽  
pp. 1-24 ◽  
Author(s):  
Grzegorz Bocewicz ◽  
Robert Wójcik ◽  
Zbigniew Antoni Banaszak ◽  
Paweł Pawlewski
Keyword(s):  
Constraint Satisfaction ◽  
Material Handling ◽  
Constraint Satisfaction Problem ◽  
Transportation Network ◽  
Cyclic Scheduling ◽  
Scheduling Problems ◽  
Multimodal Transportation ◽  
Shared Workstations ◽  
Production Orders ◽  
Space Approach

The paper concerns cyclic scheduling problems arising in a Multimodal Transportation Network (MTN) in which several unimodal networks (AGVs, hoists, lifts, etc.) interact with each other via common shared workstations as to provide a variety of demand-responsive material handling operations. The material handling transport modes provide movement of work-pieces between workstations along their manufacturing routes in the MTN. The goal is to provide a declarative framework enabling to state a constraint satisfaction problem aimed at AGVs fleet match-up scheduling taking into consideration assumed itineraries of concurrently manufactured product types. In that context, treating the different product types as a set of cyclic multimodal processes is the main objective to discuss the conditions sufficient for FMS rescheduling imposed by production orders changes. To conclude, the conditions sufficient for an FMS rescheduling imposed by changes of production orders treated as cyclic multimodal processes are stated as the paper’s main contribution.

scholarly journals A Hybrid Metaheuristic DE/CS Algorithm for UCAV Three-Dimension Path Planning

2012 ◽  
Vol 2012 ◽  
pp. 1-11 ◽  
Author(s):  
Gaige Wang ◽  
Lihong Guo ◽  
Hong Duan ◽  
Heqi Wang ◽  
Luo Liu ◽  
...  
Keyword(s):  
Path Planning ◽  
Optimization Problem ◽  
Cuckoo Search ◽  
Planning Problem ◽  
Three Dimension ◽  
New Approach ◽  
Hybrid Metaheuristic ◽  
Air Vehicle ◽  
Flight Route ◽  
Path Planning Problem

Three-dimension path planning for uninhabited combat air vehicle (UCAV) is a complicated high-dimension optimization problem, which primarily centralizes on optimizing the flight route considering the different kinds of constrains under complicated battle field environments. A new hybrid metaheuristic differential evolution (DE) and cuckoo search (CS) algorithm is proposed to solve the UCAV three-dimension path planning problem. DE is applied to optimize the process of selecting cuckoos of the improved CS model during the process of cuckoo updating in nest. The cuckoos can act as an agent in searching the optimal UCAV path. And then, the UCAV can find the safe path by connecting the chosen nodes of the coordinates while avoiding the threat areas and costing minimum fuel. This new approach can accelerate the global convergence speed while preserving the strong robustness of the basic CS. The realization procedure for this hybrid metaheuristic approach DE/CS is also presented. In order to make the optimized UCAV path more feasible, the B-Spline curve is adopted for smoothing the path. To prove the performance of this proposed hybrid metaheuristic method, it is compared with basic CS algorithm. The experiment shows that the proposed approach is more effective and feasible in UCAV three-dimension path planning than the basic CS model.

A New Multi-Objective Firework Algorithm to Solve the Multimodal Planning Network Problem

2020 ◽  
Vol 11 (4) ◽  
pp. 91-113
Author(s):  
Mouna Gargouri Mnif ◽  
Sadok Bouamama
Keyword(s):  
Optimization Problems ◽  
Transportation Network ◽  
Search Space ◽  
Search Technique ◽  
Multimodal Transportation ◽  
New Approach ◽  
Multi Objective ◽  
Network Problem ◽  
Complex Optimization ◽  
Stochastic Search Technique

This article introduces a new approach called multi-objective firework algorithm (MFWA). The proposed approach allows for solving the multimodal transportation network problem (MTNP). The main goal is to develop a decision system that optimizes and determines the planning network of the multimodal transportation (PNMT) problem. The optimization involves reaching the efficient transport mode and multimodal path, in order to move from one country to another while satisfying the set of objectives. Moreover, the firework algorithm has distinct advantages in solving complex optimization problems and in obtaining a solution by a distributed and oriented research system. This approach presents a search way, which is different from the swarm intelligence-based stochastic search technique. For each firework, the process starts by exploding a firework in the sky. The search space is filled with a shower of sparks to get diversity solutions. This new approach proves their efficacy in solving the multi-objective problem, which is shown by the experimental results.

scholarly journals Multi-Objective Optimization Methods for Transportation Network Problems

2020 ◽  
Vol 11 (1) ◽  
pp. 1-36
Author(s):  
Mouna Gargouri Mnif ◽  
Sadok Bouamama
Keyword(s):  
Transportation Problem ◽  
Transportation Network ◽  
Optimization Methods ◽  
Multi Objective Optimization ◽  
Multimodal Transportation ◽  
Literature Research ◽  
Transportation Problems ◽  
Multi Objective ◽  
Network Problems

This article recapitulates literature research solving transportation problems and these variants, notably the multimodal transportation problems variants. Moreover, the existing optimization methods critiqued and synthesized their efficiency to solve the transportation problem. This problem can be identified by various criteria and objectives functions that distinguished according to the case study. Based on the existing literature research, a taxonomy is proposed to distinguish different factors and criteria that perform and influence the multi-objective optimization on the transportation network planning problems. The transportation problems are cited according to these objective functions, and the variant of the problem by referring to the previous studies. In this article, the authors have focused their attention on a recent multi-objective mathematical model to solve the planning network of the multimodal transportation problem.

ON COMPARATIVE ANALYSIS OF PERFORMANCE OPTIMIZATION METHODS OF MULTI-PUMP UNITS

2020 ◽  
pp. 23-28
Author(s):  
A.I. Glushchenko ◽  
M.Yu. Serov
Keyword(s):  
Energy Efficiency ◽  
Control System ◽  
Comparative Analysis ◽  
Performance Optimization ◽  
Optimization Methods ◽  
Online Optimization ◽  
New Approach ◽  
Advantages And Disadvantages ◽  
Analysis Of Performance

В статье рассматривается вопрос совершенствования системы управления параллельно-работающими насосными агрегатами с целью повышения энергоэффективности их работы. Проведено сравнение и выявление недостатков существующих методов решения рассматриваемой проблемы. Предложена идея нового подхода на базе онлайн оптимизации. The problem under consideration is improvement of the energy efficiency of a control system of parallel-running pump units. Known methods used to solve this problem are considered. Their advantages and disadvantages are shown. Finally, the idea of a new approach, which is based on online optimization, is proposed.

Optimization methods for power scheduling problems in smart home: Survey

2019 ◽  
Vol 115 ◽  
pp. 109362 ◽  
Author(s):  
Sharif Naser Makhadmeh ◽  
Ahamad Tajudin Khader ◽  
Mohammed Azmi Al-Betar ◽  
Syibrah Naim ◽  
Ammar Kamal Abasi ◽  
...  
Keyword(s):  
Smart Home ◽  
Optimization Methods ◽  
Scheduling Problems ◽  
Power Scheduling

Solving Mono- and Multi-Objective Problems Using Hybrid Evolutionary Algorithms and Nelder-Mead Method

2021 ◽  
Vol 12 (4) ◽  
pp. 98-116
Author(s):  
Noureddine Boukhari ◽  
Fatima Debbat ◽  
Nicolas Monmarché ◽  
Mohamed Slimane
Keyword(s):  
Convergence Rate ◽  
Optimization Problem ◽  
Optimization Problems ◽  
State Of The Art ◽  
Hybrid Approach ◽  
Optimization Methods ◽  
Global Optimum ◽  
Stochastic Methods ◽  
Local Optima ◽  
Portfolio Optimization Problem

Evolution strategies (ES) are a family of strong stochastic methods for global optimization and have proved their capability in avoiding local optima more than other optimization methods. Many researchers have investigated different versions of the original evolution strategy with good results in a variety of optimization problems. However, the convergence rate of the algorithm to the global optimum stays asymptotic. In order to accelerate the convergence rate, a hybrid approach is proposed using the nonlinear simplex method (Nelder-Mead) and an adaptive scheme to control the local search application, and the authors demonstrate that such combination yields significantly better convergence. The new proposed method has been tested on 15 complex benchmark functions and applied to the bi-objective portfolio optimization problem and compared with other state-of-the-art techniques. Experimental results show that the performance is improved by this hybridization in terms of solution eminence and strong convergence.

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