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Artificial Bee Colony Algorithm and Its Application to Generalized Assignment Problem

There is a trend in the scientific community to model and solve complex optimization problems by employing natural metaphors. This is mainly due to inefficiency of classical optimization algorithms in solving larger scale combinatorial and/or highly non-linear problems. The situation is not much different if integer and/or discrete decision variables are required in most of the linear optimization models as well. One of the main characteristics of the classical optimization algorithms is their inflexibility to adapt the solution algorithm to a given problem. Generally a given problem is modelled in such a way that a classical algorithm like simplex algorithm can handle it. This generally requires making several assumptions which might not be easy to validate in many situations. In order to overcome these limitations more flexible and adaptable general purpose algorithms are needed. It should be easy to tailor these algorithms to model a given problem as close as to reality. Based on this motivation many nature inspired algorithms were developed in the literature like genetic algorithms, simulated annealing and tabu search. It has also been shown that these algorithms can provide far better solutions in comparison to classical algorithms. A branch of nature inspired algorithms which are known as swarm intelligence is focused on insect behaviour in order to develop some meta-heuristics which can mimic insects problem solution abilities. Ant colony optimization, particle swarm optimization, wasp nets etc. are some of the well known algorithms that mimic insect behaviour in problem modelling and solution. Artificial Bee Colony (ABC) is a relatively new member of swarm intelligence. ABC tries to model natural behaviour of real honey bees in food foraging. Honey bees use several mechanisms like waggle dance to optimally locate food sources and to search new ones. This makes them a good candidate for developing new intelligent search algorithms. In this chapter an extensive review of work on artificial bee algorithms is given. Afterwards, development of an ABC algorithm for solving generalized assignment problem which is known as NP-hard problem is presented in detail along with some comparisons. It is a well known fact that classical optimization techniques impose several limitations on solving mathematical programming and operational research models. This is mainly due to inherent solution mechanisms of these techniques. Solution strategies of classical optimization algorithms are generally depended on the type of objective and constraint

Prediction of compressive and tensile strength of limestone via genetic programming

Accurate determination of compressive and tensile strength of limestone is an important subject for the design of geotechnical structures. Although there are several classical approaches in the literature for strength prediction their predictive accuracy is generally not satisfactory. The trend in the literature is to apply artificial intelligence based soft computing techniques for complex prediction problems. Artificial neural networks which are a member of soft computing techniques were applied to strength prediction of several types of rocks in the literature with considerable success. Although artificial neural networks are successful in prediction, their inability to explicitly produce prediction equations can create difficulty in practical circumstances. Another member of soft computing family which is known as genetic programming can be a very useful candidate to overcome this problem. Genetic programming based approaches are not yet applied to the strength prediction of limestone. This paper makes an attempt to apply a promising set of genetic programming techniques which are known as multi expression programming (MEP), gene expression programming (GEP) and linear genetic programming (LGP) to the uniaxial compressive strength (UCS) and tensile strength prediction of chalky and clayey soft limestone. The data for strength prediction were generated experimentally in the University of Gaziantep civil engineering laboratories by using limestone samples collected from Gaziantep region of Turkey.

Bees algorithm for generalized assignment problem

Bees algorithm (BA) is a new member of meta-heuristics. BA tries to model natural behavior of honey bees in food foraging. Honey bees use several mechanisms like waggle dance to optimally locate food sources and to search new ones. This makes them a good candidate for developing new algorithms for solving optimization problems. In this paper a brief review of BA is first given, afterwards development of a BA for solving generalized assignment problems (GAP) with an ejection chain neighborhood mechanism is presented. GAP is a NP-hard problem. Many meta-heuristic algorithms were proposed for its solution. So far BA is generally applied to continuous optimization. In order to investigate the performance of BA on a complex integer optimization problem, an attempt is made in this paper. An extensive computational study is carried out and the results are compared with several algorithms from the literature.

Training neural networks with harmony search algorithms for classification problems

Training neural networks (NNs) is a complex task of great importance in the supervised learning area. However, performance of the NNs is mostly dependent on the success of training process, and therefore the training algorithm. This paper addresses the application of harmony search algorithms for the supervised training of feed-forward (FF) type NNs, which are frequently used for classification problems. In this paper, five different variants of harmony search algorithm are studied by giving special attention to Self-adaptive Global Best Harmony Search (SGHS) algorithm. A structure suitable to data representation of NNs is adapted to SGHS algorithm. The technique is empirically tested and verified by training NNs on six benchmark classification problems and a real-world problem. Among these benchmark problems two of them have binary classes and remaining four are n-ary classification problems. Real-world problem is related to the classification of most frequently encountered quality defect in a major textile company in Turkey. Overall training time, sum of squared errors, training and testing accuracies of SGHS algorithm, is compared with the other harmony search algorithms and the most widely used standard back-propagation (BP) algorithm. The experiments presented that the SGHS algorithm lends itself very well to the training of NNs and also highly competitive with the compared methods in terms of classification accuracy.

Bee colony intelligence in zone constrained two-sided assembly line balancing problem

Bees Algorithm is a relatively new member of swarm intelligence based meta-heuristics which tries to model natural behavior of real honey bees in food foraging. Honey bees use several mechanisms like waggle dance to optimally locate food sources and to search new ones. This makes them a good candidate for developing new search algorithms for solving optimization problems in operational research. On the other hand, two-sided assembly lines are generally occurred in assembly of large-sized products such as buses and trucks. In a two-sided assembly line, different assembly tasks are carried out on the same product in parallel to both left and right sides of the line. In this study Bees Algorithm is adopted to solve two-sided assembly line balancing problem with zoning constraint so as to minimize the number of stations for a given cycle time. An extensive computational study is carried out and the results are compared with the results of several algorithms from the literature with the results of exact solution approaches and several algorithms from the literature such as ant colony optimization, tabu search.

Using multiple objective tabu search and grammars to model and solve multi-objective flexible job shop scheduling problems

A linguistic-based meta-heuristic modeling and solution approach for solving the flexible job shop scheduling problem (FJSSP) is presented in this study. FJSSP is an extension of the classical job-shop scheduling problem. The problem definition is to assign each operation to a machine out of a set of capable machines (the routing problem) and to order the operations on the machines (the sequencing problem), such that predefined performance measures are optimized. In this research, the scope of the problem is widened by taking into account the alternative process plans for each part (process plan selection problem). Probabilistic selection of alternative process plans and machines are also considered. The FJSSP is presented as a grammar and the productions in the grammar are defined as controls (Baykasoğlu, 2002). Using these controls and Giffler and Thompsons (1960) priority rule-based heuristic along with the multiple objective tabu search algorithm of Baykasoğlu et al. (1999) FJSSP is solved. This novel approach simplifies the modeling process of the FJSSP and enables usage of existing job shop scheduling algorithms for its fast solution. Instead of scheduling job shops with inflexible algorithms that cannot take into account the flexibility which is available in the job shop, the present algorithm is developed which can take into account the flexibility during scheduling. Such an approach will considerably increase the responsiveness of the job shops.

Multiple-colony ant algorithm for parallel assembly line balancing problem

Assembly lines are designed as flow oriented production systems which perform operations on standardized products in a serial manner. Balancing of assembly lines is one of the most important problems among the other problems of assembly lines like designing and managing. In todays highly competitive manufacturing environment increasing system flexibility, reducing failure sensitivity, improving system balance and productivity are crucial. Parallel assembly lines provide some opportunities in improving these objectives especially when the capacity of production system is insufficient. Unlike the traditional assembly lines there are a few studies on balancing parallel assembly lines in the present literature. Parallel assembly line balancing is a NP-hard problem similar to other assembly lines. In this paper, a novel multiple-colony ant algorithm is developed for balancing bi-objective parallel assembly lines. The proposed algorithm is also one of the first attempts in modeling and solving the present problem with swarm intelligence based meta-heuristics. The proposed approach is extensively tested on the benchmark problems and performance of the approach is compared with existing algorithms. It is shown that the proposed approach is very effective.

Modeling and solving constrained two-sided assembly line balancing problem via bee algorithms

Designing and operating two-sided assembly lines are crucial for manufacturing companies which assemble large-sized products such as trucks, buses and industrial refrigerators. This type of assembly line structure has several advantages over one-sided assembly lines such as shortened line length and reduced throughput time. The research area has recently focused on balancing two-sided assembly lines owing to these advantages. However, due to the complex structure of this problem, some practical constraints have been disregarded or have not been fully incorporated. In order to overcome these deficiencies, a fully constrained two-sided assembly line balancing problem is addressed in this research paper. Initially, a mathematical programming model is presented in order to describe the problem formally. Due to the problem complexity, two different swarm intelligence based search algorithms are implemented to solve large-sized instances. Bees algorithm and artificial bee colony algorithm have been applied to the fully constrained two-sided assembly line balancing problem so as to minimize the number of workstations and to obtain a balanced line. An extensive computational study has also been performed and the comparative results have been evaluated.

MEPAR-miner: Multi-expression programming for classification rule mining

Classification and rule induction are two important tasks to extract knowledge from data. In rule induction, the representation of knowledge is defined as IF-THEN rules which are easily understandable and applicable by problem-domain experts. In this paper, a new chromosome representation and solution technique based on Multi-Expression Programming (MEP) which is named as MEPAR-miner (Multi-Expression Programming for Association Rule Mining) for rule induction is proposed. Multi-Expression Programming (MEP) is a relatively new technique in evolutionary programming that is first introduced in 2002 by Oltean and Dumitrescu. MEP uses linear chromosome structure. In MEP, multiple logical expressions which have different sizes are used to represent different logical rules. MEP expressions can be encoded and implemented in a flexible and efficient manner. MEP is generally applied to prediction problems; in this paper a new algorithm is presented which enables MEP to discover classification rules. The performance of the developed algorithm is tested on nine publicly available binary and n-ary classification data sets. Extensive experiments are performed to demonstrate that MEPAR-miner can discover effective classification rules that are as good as (or better than) the ones obtained by the traditional rule induction methods. It is also shown that effective gene encoding structure directly improves the predictive accuracy of logical IF-THEN rules.

Balancing fuzzy multi-objective two-sided assembly lines via Bees Algorithm

Many real life problems contain imprecise variables, constraints and objectives. Fuzzy set theory gives an opportunity to handle imprecise terms in such situations. Two-sided assembly line balancing (2sALB) problem which is a generalization of the well known simple assembly line balancing problem can also be modeled more realistically by employing fuzzy approaches. Such an approach is presented in this study to model and solve 2sALB problem by employing fuzzy mathematical programming and Bees Algorithm (BA). 2sALB problem is a combinatorial complex problem. For this reason BA is employed as a search mechanism for obtaining good solutions to it. BA is a relatively new member of swarm intelligence based meta-heuristics that tries to mimic natural behavior of real honey bees in food foraging in solving complex optimization problems. BA is generally applied to continuous optimization in the literature. Its application to combinatorial problems is rare. This study also presents one of the first application of BA to an assembly line balancing problem which is member of combinatorial optimization.