Introduction to linear and nonlinear programming

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

Keywords: LTS5 Reference EPFL-CONF-138670 Record created on 2009-05-29, modified on 2017-05-10

https://serval.unil.ch/resource/serval:BIB_E467E3E2F917.P001/REF.pdf

Mapping the human connectome at multiple scales with diffusion spectrum MRI

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

/pdf/swarm-intelligence-algorithms-for-feature-selection-a-review-148v6fova7.pdf

Swarm Intelligence Algorithms for Feature Selection: A Review

The Traveling Salesman Problem A Computational Study

African Buffalo Optimization: A Swarm-Intelligence Technique

This paper proposes the African Buffalo Optimization (ABO) which is a new metaheuristic algorithm that is derived from careful observation of the African buffalos, a species of wild cows, in the African forests and savannahs. This animal displays uncommon intelligence, strategic organizational skills, and exceptional navigational ingenuity in its traversal of the African landscape in search for food. The African Buffalo Optimization builds a mathematical model from the behavior of this animal and uses the model to solve 33 benchmark symmetric Traveling Salesman's Problem and six difficult asymmetric instances from the TSPLIB. This study shows that buffalos are able to ensure excellent exploration and exploitation of the search space through regular communication, cooperation, and good memory of its previous personal exploits as well as tapping from the herd's collective exploits. The results obtained by using the ABO to solve these TSP cases were benchmarked against the results obtained by using other popular algorithms. The results obtained using the African Buffalo Optimization algorithm are very competitive.

/pdf/solving-the-traveling-salesman-s-problem-using-the-african-3x8fl5nx2o.pdf

Solving the Traveling Salesman's Problem using the African Buffalo Optimization

In this paper, an attempt is made to apply the African Buffalo Optimization (ABO) to tune the parameters of a PID controller for an effective Automatic Voltage Regulator (AVR). Existing metaheuristic tuning methods have been proven to be quite successful but there were observable areas that need improvements especially in terms of the system’s gain overshoot and steady steady state errors. Using the ABO algorithm where each buffalo location in the herd is a candidate solution to the Proportional-Integral-Derivative parameters was very helpful in addressing these two areas of concern. The encouraging results obtained from the simulation of the PID Controller parameters-tuning using the ABO when compared with the performance of Genetic Algorithm PID (GA-PID), Particle-Swarm Optimization PID (PSO-PID), Ant Colony Optimization PID (ACO-PID), PID, Bacteria-Foraging Optimization PID (BFO-PID) etc makes ABO-PID a good addition to solving PID Controller tuning problems using metaheuristics.

/pdf/parameters-tuning-of-pid-controller-for-automatic-voltage-3rizk8cdna.pdf

Parameters-tuning of PID controller for automatic voltage regulators using the African buffalo optimization.

Numerical Function Optimization Solutions Using the AfricanBuffalo Optimization Algorithm (ABO)

This is an introductory paper to the newly-designed African Buffalo Optimization (ABO) algorithm for solving combinatorial and other optimization problems. The algorithm is inspired by the behavior of African buffalos, a species of wild cows known for their extensive migrant lifestyle. This paper presents an overview of major metaheuristic algorithms with the aim of providing a basis for the development of the African Buffalo Optimization algorithm which is a nature-inspired, population-based metaheuristic algorithm. Experimental results obtained from applying the novel ABO to solve a number of benchmark global optimization test functions as well as some symmetric and asymmetric Traveling Salesman’s Problems when compared to the results obtained from using other popular optimization methods show that the African Buffalo Optimization is a worthy addition to the growing number of swarm intelligence optimization techniques. Keywords: Graphite; African Buffalo Optimization; Metaheuristics; population-based; global optimization; Traveling Salesman’s Problem.

/pdf/african-buffalo-optimization-45fo48iozy.pdf

Julius Beneoluchi Odili

Papers

African Buffalo Optimization: A Swarm-Intelligence Technique

Solving the Traveling Salesman's Problem using the African Buffalo Optimization

Parameters-tuning of PID controller for automatic voltage regulators using the African buffalo optimization.

Numerical Function Optimization Solutions Using the AfricanBuffalo Optimization Algorithm (ABO)

African buffalo optimization