Metaheuristic design of feedforward neural networks: a review of two decades of research

Over the past two decades, the feedforward neural network (FNN) optimization has been a key interest among the researchers and practitioners of multiple disciplines. The FNN optimization is often viewed from the various perspectives: the optimization of weights, network architecture, activation nodes, learning parameters, learning environment, etc. Researchers adopted such different viewpoints mainly to improve the FNN's generalization ability. The gradient-descent algorithm such as backpropagation has been widely applied to optimize the FNNs. Its success is evident from the FNN's application to numerous real-world problems. However, due to the limitations of the gradient-based optimization methods, the metaheuristic algorithms including the evolutionary algorithms, swarm intelligence, etc., are still being widely explored by the researchers aiming to obtain generalized FNN for a given problem. This article attempts to summarize a broad spectrum of FNN optimization methodologies including conventional and metaheuristic approaches. This article also tries to connect various research directions emerged out of the FNN optimization practices, such as evolving neural network (NN), cooperative coevolution NN, complex-valued NN, deep learning, extreme learning machine, quantum NN, etc. Additionally, it provides interesting research challenges for future research to cope-up with the present information processing era

Penerapan Metode Kombinasi Algoritma Genetika dan Tabu Search dalam Optimasi Alokasi Kapal Peti Kemas (Studi Kasus : PT. XYZ)

Perkembangan perdagangan global menyebabkan penggunaan jasa transportasi menjadi bagian yang sangat penting dalam pendistribusian barang. Salah satunya yaitu jasa transportasi laut atau jasa pelayaran. Dengan terus berkembangnya jasa pelayaran, maka perlu adanya perencanaan dan keputusan-keputusan yang tepat dalam pengalokasian kapal yang akan digunakan dalam proses pengiriman barang. Oleh karena itu dalam penelitian ini akan dilakukan optimalisasi alokasi kapal pada PT.XYZ dengan tujuan memaksimalkan profit dan memaksimalkan kapasitas menggunakan metode kombinasi algoritma genrtika dan tabu search. Penggunaan metode kombinasi algoritma genetika dan tabu search pada pengalokasian kapal bertujuan untuk menemukan solusi yang optimum dalam mengalokasikan kapal. Berdasarkan perbandingan antara metode algortima genetika (GA) dan metode kombinasi algoritma genetika dan tabu search (GA-TS), diperoleh hasil profit dan muatan yang lebih optimal ketika menggunakan metode GA-TS dengan peningkatan profit sebesar 69% dan peningkatan load factor sebesar 14%. Peningkatan profit dan load factor juga ditunjukkan ketika dilakukan perbandingan antara kondisi pada Perusahaan sebelum menerapkan GA-TS dan sesudah menerapkan GA-TS. Metode GA-TS memiliki profit dengan peningkatan lebih dari 100% dan peningkatan load factor sebesar 38% dibanding pada kondisi Perusahaan. Sehingga berdasarkan hal ini, implementasi algoritma genetika dan tabu search dapat menjadi solusi bagi Perusahaan dan membantu Perusahaan dalam membuat perencanaan pengalokasian kapal

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

Particle swarm optimization (PSO) is a heuristic global optimization method, proposed originally by Kennedy and Eberhart in 1995. It is now one of the most commonly used optimization techniques. This survey presented a comprehensive investigation of PSO. On one hand, we provided advances with PSO, including its modifications (including quantum-behaved PSO, bare-bones PSO, chaotic PSO, and fuzzy PSO), population topology (as fully connected, von Neumann, ring, star, random, etc.), hybridization (with genetic algorithm, simulated annealing, Tabu search, artificial immune system, ant colony algorithm, artificial bee colony, differential evolution, harmonic search, and biogeography-based optimization), extensions (to multiobjective, constrained, discrete, and binary optimization), theoretical analysis (parameter selection and tuning, and convergence analysis), and parallel implementation (in multicore, multiprocessor, GPU, and cloud computing forms). On the other hand, we offered a survey on applications of PSO to the following eight fields: electrical and electronic engineering, automation control systems, communication theory, operations research, mechanical engineering, fuel and energy, medicine, chemistry, and biology. It is hoped that this survey would be beneficial for the researchers studying PSO algorithms

Imitating individualized facial expressions in a human-like avatar through a hybrid particle swarm optimization - tabu search algorithm

This thesis describes a machine learning method for automatically imitating a particular person\u27s facial expressions in a human-like avatar through a hybrid Particle Swarm Optimization - Tabu Search algorithm. The muscular structures of the facial expressions are measured by Ekman and Friesen\u27s Facial Action Coding System (FACS). Using a neutral face as a reference, the minute movements of the Action Units, used in FACS, are automatically tracked and mapped onto the avatar using a hybrid method. The hybrid algorithm is composed of Kennedy and Eberhart\u27s Particle Swarm Optimization algorithm (PSO) and Glover\u27s Tabu Search (TS). Distinguishable features portrayed on the avatar ensure a personalized, realistic imitation of the facial expressions. To evaluate the feasibility of using PSO-TS in this approach, a fundamental proof-of-concept test is employed on the system using the OGRE avatar. This method is analyzed in-depth to ensure its proper functionality and evaluate its performance compared to previous work

A Comprehensive Review of Bio-Inspired Optimization Algorithms Including Applications in Microelectronics and Nanophotonics

The application of artificial intelligence in everyday life is becoming all-pervasive and unavoidable. Within that vast field, a special place belongs to biomimetic/bio-inspired algorithms for multiparameter optimization, which find their use in a large number of areas. Novel methods and advances are being published at an accelerated pace. Because of that, in spite of the fact that there are a lot of surveys and reviews in the field, they quickly become dated. Thus, it is of importance to keep pace with the current developments. In this review, we first consider a possible classification of bio-inspired multiparameter optimization methods because papers dedicated to that area are relatively scarce and often contradictory. We proceed by describing in some detail some more prominent approaches, as well as those most recently published. Finally, we consider the use of biomimetic algorithms in two related wide fields, namely microelectronics (including circuit design optimization) and nanophotonics (including inverse design of structures such as photonic crystals, nanoplasmonic configurations and metamaterials). We attempted to keep this broad survey self-contained so it can be of use not only to scholars in the related fields, but also to all those interested in the latest developments in this attractive area