Swarm intelligence (SI) is briefly defined as the collective behaviour of decentralized and self-organized swarms. The well known examples for these swarms are bird flocks, fish schools and the colony of social insects such as termites, ants and bees. In 1990s, especially two approaches based on ant colony and on fish schooling/bird flocking introduced have highly attracted the interest of researchers. Although the self-organization features are required by SI are strongly and clearly seen in honey bee colonies, unfortunately the researchers have recently started to be interested in the behaviour of these swarm systems to describe new intelligent approaches, especially from the beginning of 2000s. During a decade, several algorithms have been developed depending on different intelligent behaviours of honey bee swarms. Among those, artificial bee colony (ABC) is the one which has been most widely studied on and applied to solve the real world problems, so far. Day by day the number of researchers being interested in ABC algorithm increases rapidly. This work presents a comprehensive survey of the advances with ABC and its applications. It is hoped that this survey would be very beneficial for the researchers studying on SI, particularly ABC algorithm.

A comprehensive survey: artificial bee colony (ABC) algorithm and applications

Because of successful implementations and high intensity, metaheuristic research has been extensively reported in literature, which covers algorithms, applications, comparisons, and analysis. Though, little has been evidenced on insightful analysis of metaheuristic performance issues, and it is still a “black box” that why certain metaheuristics perform better on specific optimization problems and not as good on others. The performance related analyses performed on algorithms are mostly quantitative via performance validation metrics like mean error, standard deviation, and co-relations have been used. Moreover, the performance tests are often performed on specific benchmark functions—few studies are those which involve real data from scientific or engineering optimization problems. In order to draw a comprehensive picture of metaheuristic research, this paper performs a survey of metaheuristic research in literature which consists of 1222 publications from year 1983 to 2016 (33 years). Based on the collected evidence, this paper addresses four dimensions of metaheuristic research: introduction of new algorithms, modifications and hybrids, comparisons and analysis, and research gaps and future directions. The objective is to highlight potential open questions and critical issues raised in literature. The work provides guidance for future research to be conducted more meaningfully that can serve for the good of this area of research.

Metaheuristic research: a comprehensive survey

A survey on new generation metaheuristic algorithms

In recent years, various metaheuristic optimization methods have been proposed in scientific and engineering fields In this study, a novel physics-inspired metaheuristic optimization algorithm, atom search optimization (ASO), inspired by basic molecular dynamics, is developed to address a diverse set of optimization problems ASO mathematically models and mimics the atomic motion model in nature, where atoms interact through interaction forces resulting from the Lennard-Jones potential and constraint forces resulting from the bond-length potential The proposed algorithm is simple and easy to implement ASO is tested on a range of benchmark functions to verify its validity, qualitatively and quantitatively, and then applied to a hydrogeologic parameter estimation problem with success The results demonstrate that ASO is superior to some classic and newly emerging algorithms in the literature and is a promising solution to real-world engineering problems

Atom search optimization and its application to solve a hydrogeologic parameter estimation problem

The increasingly rapid creation, sharing and exchange of information nowadays put researchers and data scientists ahead of a challenging task of data analysis and extracting relevant information out of data To be able to learn from data, the dimensionality of the data should be reduced first Feature selection (FS) can help to reduce the amount of data, but it is a very complex and computationally demanding task, especially in the case of high-dimensional datasets Swarm intelligence (SI) has been proved as a technique which can solve NP-hard (Non-deterministic Polynomial time) computational problems It is gaining popularity in solving different optimization problems and has been used successfully for FS in some applications With the lack of comprehensive surveys in this field, it was our objective to fill the gap in coverage of SI algorithms for FS We performed a comprehensive literature review of SI algorithms and provide a detailed overview of 64 different SI algorithms for FS, organized into eight major taxonomic categories We propose a unified SI framework and use it to explain different approaches to FS Different methods, techniques, and their settings are explained, which have been used for various FS aspects The datasets used most frequently for the evaluation of SI algorithms for FS are presented, as well as the most common application areas The guidelines on how to develop SI approaches for FS are provided to support researchers and analysts in their data mining tasks and endeavors while existing issues and open questions are being discussed In this manner, using the proposed framework and the provided explanations, one should be able to design an SI approach to be used for a specific FS problem

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Swarm Intelligence Algorithms for Feature Selection: A Review

Migrating Birds Optimization: A new metaheuristic approach and its performance on quadratic assignment problem

Combining and solving sequence dependent traveling salesman and quadratic assignment problems in PCB assembly

In this study we propose a new nature inspired metaheuristic approach based on the V formation flight of the migrating birds which is proven to be an effective formation in energy minimization. Its performance is tested on quadratic assignment problem instances arising from a real life problem and very good results are obtained. The quality of the solutions turned out to be better than simulated annealing, tabu search and guided evolutionary simulated annealing approaches. These results indicate that our new metaheuristic approach could be an important player in metaheuristic based optimization.

Migrating birds optimization: a new meta-heuristic approach and its application to the quadratic assignment problem

Optimization issues regarding the automated assembly of printed circuit boards attracted the interest of researchers for several decades. This is because even small gains in assembly time result in very important benefits in mass production. In this paper, the focus is on a particular placement machine type that has a rotational turret and a stationary component magazine. So far, this type of machine received little attention among the researchers. In this paper, the feeder configuration, placement sequencing, and assembly time minimization problems are formulated explicitly and completely (without simplifying assumptions) using nonlinear integer programming. In addition, the placement sequencing problem is shown to be a recently introduced new generalization of the traveling salesman problem (the sequence-dependent traveling salesman). These formulations show the complexity of the problems and the need for effective heuristic designs for solving them. We propose three heuristics that improve previously suggested solution methods and give comparable results when compared to simulated annealing that is a widely accepted good performing metaheuristic on combinatorial optimization problems. The heuristics are experimentally shown to improve previous methods significantly in assembly time that implies a huge economic benefit. The heuristics proposed could also be applied to other placement machines with similar operation principles.

Application of Sequence-Dependent Traveling Salesman Problem in Printed Circuit Board Assembly

By the automation of manufacturing, production of a good becomes a bunch of steps and sequences like; bolt sequencing, cutting, drilling and welding process steps. Most of these sequencing type engineering problems can be modeled as traveling salesman problems (TSP), which is widely known in operation research area. Even though much kind of methods and algorithms are developed for TSP, a few of them are applied to engineering problems. This study aims to use a deterministic approach (record-to- record travel algorithm) to solve TSP modeled engineering problems. Bolt sequencing and drilling path problems are chosen from literature as test problems. Results show performance of the proposed algorithm

Ali Fuat Alkaya

Papers

Migrating Birds Optimization: A new metaheuristic approach and its performance on quadratic assignment problem

Combining and solving sequence dependent traveling salesman and quadratic assignment problems in PCB assembly

Migrating birds optimization: a new meta-heuristic approach and its application to the quadratic assignment problem

Application of Sequence-Dependent Traveling Salesman Problem in Printed Circuit Board Assembly

Deterministic Approach to Path Optimization Problem