scholarly journals An algorithm for multiparametric 0-1-Integer Programming problems relative to a generalized min max objective function

2009 ◽  
Vol 43 (1) ◽  
pp. 1-12 ◽  
Author(s):  
José Luis Quintero ◽  
Alejandro Crema
Keyword(s):  
Integer Programming ◽  
Objective Function ◽  
Integer Programming Problems

Performance-Enhancing Techniques

2010 ◽  
pp. 133-148
Author(s):  
E. Parsopoulos Konstantinos ◽  
N. Vrahatis Michael
Keyword(s):  
Integer Programming ◽  
Objective Function ◽  
Penalty Function ◽  
Optimization Problems ◽  
Function Approach ◽  
Integer Optimization ◽  
Local Minimizers ◽  
Global Minimizers ◽  
Integer Programming Problems ◽  
Penalty Function Approach

This chapter presents techniques that have proved to be very useful in enhancing the performance of PSO in various optimization problem types. They consist of transformations of either the objective function or the problem variables, enabling PSO to alleviate local minimizers, detect multiple minimizers, handle constraints, and solve integer optimization problems. The chapter begins with a short discussion on the filled functions approach, and then presents the stretching technique as an alternative for alleviating local minimizers. Next, we present the deflection and repulsion techniques, as a means for detecting multiple global minimizers with PSO, followed by a penalty function approach for constraint handling. The chapter closes with the description of two rounding schemes that enable the continuous, real-valued PSO to solve integer programming problems. All techniques are thoroughly described and graphically illustrated whenever possible.

scholarly journals A Shuffle-Based Artificial Bee Colony Algorithm for Solving Integer Programming and Minimax Problems

Mathematics ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 1211
Author(s):  
Ivona Brajević
Keyword(s):  
Integer Programming ◽  
Artificial Bee Colony ◽  
Optimization Problems ◽  
Minimax Problems ◽  
Abc Algorithm ◽  
Bee Colony ◽  
Effective Performance ◽  
Complex Optimization ◽  
Overall Performance ◽  
Integer Programming Problems

The artificial bee colony (ABC) algorithm is a prominent swarm intelligence technique due to its simple structure and effective performance. However, the ABC algorithm has a slow convergence rate when it is used to solve complex optimization problems since its solution search equation is more of an exploration than exploitation operator. This paper presents an improved ABC algorithm for solving integer programming and minimax problems. The proposed approach employs a modified ABC search operator, which exploits the useful information of the current best solution in the onlooker phase with the intention of improving its exploitation tendency. Furthermore, the shuffle mutation operator is applied to the created solutions in both bee phases to help the search achieve a better balance between the global exploration and local exploitation abilities and to provide a valuable convergence speed. The experimental results, obtained by testing on seven integer programming problems and ten minimax problems, show that the overall performance of the proposed approach is superior to the ABC. Additionally, it obtains competitive results compared with other state-of-the-art algorithms.

scholarly journals Transportation with volume discount: a case study of a logistic operator in Ghana

2014 ◽  
Vol 8 (2) ◽  
pp. 7-37
Author(s):  
Abdul-Salam Sibidoo Mubashiru
Keyword(s):  
Integer Programming ◽  
Production Scheduling ◽  
Transportation Problem ◽  
Transportation Network ◽  
Supply And Demand ◽  
Network Models ◽  
Cost Effective ◽  
Capital Budgeting ◽  
Integer Programming Problems ◽  
Optimality Algorithm

Network models and integer programming are well known variety of decision making problems. A very useful and widespread area of application is the management and efficient use of scarce resources to increase productivity. These applications include operational problems such as the distributions of goods, production scheduling and machine sequencing, and planning problems such as capital budgeting facility allocation, portfolio selection, and design problems such as telecommunication and transportation network design. The transportation problem, which is one of network integer programming problems is a problem that deals with distributing any commodity from any group of 'sources' to any group of destinations or 'sinks' in the most cost effective way with a given 'supply' and 'demand' constraints. Depending on the nature of the cost function, the transportation problem can be categorized into linear and nonlinear transportation problem. We applied Karush-Kuhn-Tucker (KKT) optimality algorithm to solve our problem of transportation with volume discount for a logistic operator in Ghana.

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