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An improved cuckoo search algorithm for integer programming problems

INTERNATIONAL JOURNAL OF COMPUTING SCIENCE AND MATHEMATICS 2018年第1期9卷 66-81页

作者： Abdel-Baset, Mohamed Zhou, Yongquan Ismail, Mahmoud Zagazig Univ Fac Comp & Informat Dept Operat Res El Zera Sq Zagazig 44519 Egypt Guangxi Univ Nationalities Coll Informat Sci & Engn Nanning 530006 Peoples R China

This paper presents a novel cuckoo search algorithm called elite opposition - cuckoo search algorithm (ECS) for solving integer programming problems. The opposite solution of the elite individual in the population is generated by an opposition-based strategy in the proposed algorithm and form an opposite search space by constructing the opposite population that locates inside the dynamic search boundaries, then, the search space of the algorithm is guided to approximate the space in which the global optimum is included by simultaneously evaluating the current population and the opposite one. The results show that ECS algorithm has faster convergence speed, higher computational precision and is more effective for solving integer programming problems.

关键词： elite opposition cuckoo search integer programming problems

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Multidirectional harmony search algorithm for solving integer programming and minimax problems

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INTERNATIONAL JOURNAL OF BIO-INSPIRED COMPUTATION 2019年第3期13卷 141-158页

作者： Tawhid, Mohamed A. Ali, Ahmed F. Thompson Rivers Univ Fac Sci Dept Math & Stat Kamloops BC V2C 0C8 Canada Suez Canal Univ Fac Comp & Informat Dept Comp Sci Ismailia Egypt

integer programming and minimax problems are essential tools in solving various problems that arise in data mining and machine learning such as multi-class data classification and feature selection problems. In this paper, we propose a new hybrid harmony search algorithm by combining the harmony search algorithm with the multidirectional search method in order to solve the integer programming and minimax problems. The proposed algorithm is called multidirectional harmony search algorithm (MDHSA). MDHSA starts the search by applying the standard harmony search for numbers of iteration then the best-obtained solution is passing to the multidirectional search method as an intensification process in order to accelerate the search and overcome the slow convergence of the standard harmony search algorithm. The proposed algorithm is balancing between the global exploration of the harmony search algorithm and the deep exploitation of the multidirectional search method. MDHSA algorithm is tested on seven integer programming problems and 15 minimax problems and compared against 12 algorithms for solving integer programming problems and 11 algorithms for solving minimax problems. The experiments results show the efficiency of the proposed algorithm and its ability to solve integer programming and minimax problems in reasonable time.

关键词： evolutionary computation global optimisation harmony search algorithm direct search algorithm multidirectional search integer programming problems minimax problems

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A simplex social spider algorithm for solving integer programming and minimax problems

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MEMETIC COMPUTING 2016年第3期8卷 169-188页

作者： Tawhid, Mohamed A. Ali, Ahmed F. Thompson Rivers Univ Fac Sci Dept Math & Stat Kamloops BC V2C 0C8 Canada Univ Alexandria Fac Sci Dept Math & Comp Sci Alexandria 21511 Egypt Suez Canal Univ Fac Comp & Informat Dept Comp Sci Ismailia Egypt

In this paper, we propose a new hybrid social spider algorithm with simplex Nelder-Mead method in order to solve integer programming and minimax problems. We call the proposed algorithm a Simplex Social Spider optimization (SSSO) algorithm. In the the proposed SSSO algorithm, we combine the social spider algorithm with its powerful capability of performing exploration, exploitation, and the Nelder-Mead method in order to refine the best obtained solution from the standard social spider algorithm. In order to investigate the general performance of the proposed SSSO algorithm, we test it on 7 integer programming problems and 10 minimax problems and compare against 10 algorithms for solving integer programming problems and 9 algorithms for solving minimax problems. The experiments results show the efficiency of the proposed algorithm and its ability to solve integer and minimax optimization problems in reasonable time.

关键词： Social spider algorithm Direct search algorithm Simplex Nelder-Mead method integer programming problems Minimax problems

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A greedy randomized adaptive search procedure for transmission expansion planning

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IEEE TRANSACTIONS ON POWER SYSTEMS 2001年第2期16卷 247-253页

作者： Binato, S de Oliveira, GC de Araújo, JL CEPEL Brazilian Elect Power Res ctr BR-21944970 Rio De Janeiro Brazil Pontificia Univ Catolica Rio de Janeiro BR-22453 Rio De Janeiro Brazil Univ Fed Rio de Janeiro BR-21945 Rio De Janeiro Brazil

A Greedy Randomized Adaptive Search Procedure (GRASP) is a heuristic method that has shown to be very powerful in solving combinatorial problems. In this paper we apply GRASP to solve the transmission network expansion problem. This procedure is an expert iterative sampling technique that has two phases for each iteration. The first, construction phase, finds a feasible solution for the problem. The second phase, a local search, seeks for improvements on construction phase solution by a local search. The best solution over all GRASP iterations is chosen as the result.

关键词： heuristic methods integer programming problems transmission network expansion planning

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Maximisation of expected target damage value

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DEFENCE SCIENCE JOURNAL 2005年第2期55卷 133-139页

作者： Deep, K Pant, M Indian Inst Technol Roorkee 247667 Uttar Pradesh India

The weapon assignment problem has been modelled as a nonlinear integer programming problem(1). The problem is to assign weapons to the targets to maximise the optimum-target damage value. There are constraints on various types of weapons available and on minimum number of weapons by types to be assigned to various targets. The objective function is nonlinear, the constraints are linear in nature, and the decision variables are restricted to be integers. The results obtained by Bracken and McCormick(1) should not be applied to solve the problem of weapon assignment to target to maximise the optimum target damage value, because firstly, the results violate the constraints, and secondly, instead of using the integer programming techniques, the crude method of rounding off has been used to obtain the solution. In this study, the I-GRST algorithm developed by Deep and Pant(2,3) has been used to solve the weapon assignment problem. The results obtained are better than the results obtained by Bracken and McCormick(1) and also do not violate any constraints.

关键词： weapon assignment optimisation integer programming problems genetic algorithm target damage I-GRST algorithm

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Multidirectional Grey Wolf Optimizer Algorithm for Solving Global Optimization problems

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INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE AND APPLICATIONS 2018年第4期17卷

作者： Tawhid, Mohamed A. Ali, Ahmed F. Thompson Rivers Univ Dept Math & Stat Fac Sci Kamloops BC V2C 0C8 Canada Suez Canal Univ Fac Comp & Informat Dept Comp Sci Ismailia Egypt

In this paper, we propose a new hybrid population-based meta-heuristics algorithm inspired by grey wolves in order to solve integer programming and minimax problems. The proposed algorithm is called Multidirectional Grey Wolf Optimizer (MDGWO) algorithm. In the proposed algorithm, we try to accelerate the standard grey wolf optimizer algorithm (GWO) by invoking the multidirectional search method with it in order to accelerate the search instead of letting the standard GWO run for more iterations without significant improvement in the results. MDGWO starts the search by applying the standard GWO search for a number of iterations, and then the best-obtained solution is passed to the multidirectional search method as an intensification process in order to accelerate the search and overcome the slow convergence of the standard GWO algorithm. We test MDGWO algorithm on seven integer programming problems and 10 minimax problems. Moreover, we compare against 11 algorithms for solving integer programming problems and 10 algorithms for solving minimax problems. Furthermore, we show the efficiency of the proposed algorithm and its ability to solve integer and minimax optimization problems in reasonable time by giving several results of the experiments.

关键词： Grey wolf optimizer direct search algorithm multidirectional search global optimization integer programming problems minimax problems

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Data envelopment analysis: an application in the transport sector

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JOURNAL OF INTERDISCIPLINARY MATHEMATICS 2006年第2期9卷 385-395页

作者： Saxena, Punita Dewan, K. K. Mustafa, M. Univ Delhi Shaheed Rajguru Coll Appl Sci Women Dept Math Jhilmil ColonyVivek Vihar Delhi 110095 India Cent Univ Jamia Millia Islamia Dept Math New Delhi 110025 India Cent Univ Jamia Millia Islamia Dept Commerce & Business Studies New Delhi 110025 India

Passenger transportation is an important part of the overall development problem of the nation and affects in some way nearly all aspects of mobility in general. This paper deals with the problems caused due to a decline in the performance of the State Transport Undertakings with a special reference to Delhi Transport Corporation. A Linear programming based technique called as Data Envelopment Analysis (DEA) is used to measure the efficiencies of various decision-making units. The analysis on this study is sought to provide a way to obtain a valid efficiency measure for each State Transport Undertaking.

关键词： integer programming problems data envelopment analysis transportation problem

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Enumeration technique for set covering problems: a combinatorial approach

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JOURNAL OF INTERDISCIPLINARY MATHEMATICS 2006年第2期9卷 421-430页

作者： Saxena, Ratnesh Rajan Univ Delhi Deen Dayal Upadhyaya Coll Dept Math Karam Pura Delhi 110015 India

Set Covering problems belong to a class of 0-1 integer programming problems which are NP-complete. The Set Covering problems have many applications such as location of emergency facilities, truck deliveries, political districting, Air Line Crew Scheduling, Networking and all other programming problems that need the decision variables of the form 0-1. In this paper an enumeration technique is developed to solve the Set Covering Problem using the combinatorial technique. The well-known Breadth First Search technique of graph theory forms the basis of the algorithm. The Set Covering problems with linear and nonlinear objective functions are discussed. At the end, the concept is extended for Multi-Objective Set Covering Problem. The algorithms developed in the paper are supported by numerical examples.

关键词： integer programming problems multi-objective programming combinatroics

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Simplex particle swarm optimization with arithmetical crossover for solving global optimization problems

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OPSEARCH 2016年第4期53卷 705-740页

In this paper, we propose a new hybrid algorithm by combining the particle swarm optimization with a genetic arithmetical crossover operator after applying a modification on it in order to avoid the problem of stagnation and premature convergence of the population. In the final stage of the algorithm, we applied the Nelder-Mead method as a local search method in order to accelerate the convergence and avoid running the algorithm without any improvements in the results. We call the new proposed algorithm by simplex particle swarm optimization with a modified arithmetical crossover (SPSOAC). We test SPSOAC on 7 integer programming optimization benchmark functions, 10 minimax problems and 10 CEC05 functions. We present the general performance of the proposed algorithm by comparing SPSOAC against 13 benchmark algorithms. The Experiments results show the proposed algorithm is a promising algorithm and has a powerful performance.

关键词： Particle swarm optimization Genetic algorithm Arithmetical crossover Nelder-Mead method integer programming problems Minimax problems

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Memetic Artificial Bee Colony for integer programming 2

Memetic Artificial Bee Colony for Integer Programming

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2nd International Conference on Advanced Machine Learning Technologies and Applications (AMLTA)

作者： Ali, Ahmed Fouad Hassanien, Aboul Ella Snasel, Vaclav Suez Canal Univ Dept Comp Sci Fac Comp & Informat Ismailia Egypt Cairo Univ Fac Comp & Informat Cairo Egypt VSB Tech Univ Ostrava Sci Res Grp Egypt Ostrava Czech Republic

ISBN: (纸本)9783319134611;9783319134604

Due to the simplicity of the Artificial Bee Colony (ABC) algorithm, it has been applied to solve a large number of problems. ABC is a stochastic algorithm and it generates trial solutions with random moves, however it suffers from slow convergence. In order to accelerate the convergence of the ABC algorithm, we proposed a new hybrid algorithm, which is called Memetic Artificial Bee Colony for integer programming (MABCIP). The proposed algorithm is a hybrid algorithm between the ABC algorithm and a Random Walk with Direction Exploitation (RWDE) as a local search method. MABCIP is tested on 7 benchmark functions and compared with 4 particle swarm optimization algorithms. The numerical results demonstrate that MABCIP is an efficient and robust algorithm.

关键词： Artificial bee colony local search algorithm memetic algorithm integer programming problems

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