DeepHive: A multi-agent reinforcement learning approach for automated discovery of swarm-based optimization policies

We present an approach for designing swarm-based optimizers for the global optimization of expensive black-box functions. In the proposed approach, the problem of finding efficient optimizers is framed as a reinforcement learning problem, where the goal is to find optimization policies that require a few function evaluations to converge to the global optimum. The state of each agent within the swarm is defined as its current position and function value within a design space and the agents learn to take favorable actions that maximize reward, which is based on the final value of the objective function. The proposed approach is tested on various benchmark optimization functions and compared to the performance of other global optimization strategies. Furthermore, the effect of changing the number of agents, as well as the generalization capabilities of the trained agents are investigated. The results show superior performance compared to the other optimizers, desired scaling when the number of agents is varied, and acceptable performance even when applied to unseen functions. On a broader scale, the results show promise for the rapid development of domain-specific optimizers

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

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

Investigating Machine Learning Techniques for Solving Product-line Optimization Problems

Product-line optimization using consumers’ preferences measured by conjoint analysis is an important issue to marketing researchers. Since it is a combinatorial NP-hard optimization problem, several meta-heuristics have been proposed to ensure at least near-optimal solutions. This work presents already used meta-heuristics in the context of product-line optimization like genetic algorithms, simulated annealing, particle-swarm optimization, and ant-colony optimization. Furthermore, other promising approaches like harmony search, multiverse optimizer and memetic algorithms are introduced to the topic. All of these algorithms are applied to a function for maximizing profits with a probabilistic choice rule. The performances of the meta-heuristics are measured in terms of best and average solution quality. To determine the most suitable metaheuristics for the underlying objective function, a Monte Carlo simulation for several different problem instances with simulated data is performed. Simulation results suggest the use of genetic algorithms, simulated annealing and memetic algorithms for product-line optimization

Enhanced Particle Swarm Optimization-Based Models And Their Application To License Plate Recognition

Model pengecaman corak memainkan peranan yang penting dalam banyak aplikasi dunia sebenar seperti pengesanan teks dan pengecaman objek. Pelbagai kaedah termasuk model Kecerdikan Berkomputer (CI) telah dibangunkan untuk menangani masalah pengecaman corak berasaskan imej. Tertumpu kepada model CI, penyelidikan ini mempersembah model berasaskan pengoptimuman kawanan zarah (PSO) yang cekap serta aplikasinya untuk pengecaman lesen plat. Pertama, model Pengoptimuman Kawanan Zarah Memetik berasaskan pengukuhan pembelajaran yang baharu (RLMPSO) diperkenalkan. Masalah pengoptimuman penanda aras digunakan untuk menilai prestasi RLMPSO, dan kaedah bootstarp digunakan untuk menilai keputusan secara statistik. Kedua, RLMPSO disepadukan dengan mesin Penyokong Vektor Kabur (FSVM) untuk merumuskan model RLMPSO-FSVM yang cekap. Secara khusus, RLMPSO-FSVM terdiri daripada gabungan pengelas linear FSVM yang dibina menggunakan RLMPSO untuk melaksanakan penalaan parameter, pemilihan ciri, serta pemilihan contoh latihan. Untuk menilai prestasi model RLMPSO-FSVM yang dicadangkan, pangkalan data imej penanda aras digunakan. Ketiga, model dua-peringkat RLMPSO-FSVM dicipta untuk mempertingkatkan lagi kecekapan. Ia mengandungi peringkat pengecaman global dan peringkat pengesahan tempatan. Peningkatan model RLMPSO turut diperkenalkan dengan memasukkan operasi carian tambahan. Model RLMPSO yang (ERLMPSO) dipertingkatkan terdiri daripada tiga lapisan, iaitu lapisan global dengan empat operasi carian, lapisan tempatan dengan satu operasi carian, dan lapisan berasaskan komponen dengan dua belas operasi carian. Akhir sekali, model dua-peringkat ERLMPSO-FSVM yang dicadangkan telah digunapakai dalam masalah Pengecaman Plat Lesen Kereta Malaysia (VLPR) yang sebenar. Kadar pengecaman setinggi 98.1% telah diperoleh. Keputusan ini mengesahkan keberkesanan model dua-peringkat ERLMPSO-FSVM yang dicadangkan dalam menangani masalah pengecaman plat lesen. ________________________________________________________________________________________________________________________ Pattern recognition models play an important role in many real-world applications such as text detection and object recognition. Numerous methodologies including Computational Intelligence (CI) models have been developed in the literature to tackle image-based pattern recognition problems. Focused on CI models, this research presents efficient Particle Swarm Optimization (PSO)-based models and their application to license plate recognition. Firstly, a new Reinforcement Learningbased Memetic Particle Swarm Optimization (RLMPSO) model is introduced. To assess the performance of RLMPSO, benchmark optimization problems are employed, and the bootstrap method is used to quantify the results statistically. Secondly, RLMPSO is integrated with the Fuzzy Support Vector Machine (FSVM) to formulate an efficient RLMPSO-FSVM model. Specifically, RLMPSO-FSVM comprises an ensemble of linear FSVM classifiers that are constructed using RLMPSO to perform parameter tuning, feature selection, as well as training sample selection. To evaluate the performance of the proposed RLMPSO-FSVM model, a benchmark image database is employed. Thirdly, to further improve efficiency, a two-stage RLMPSO-FSVM model is devised. It consists of a global recognition stage and a local verification stage. In addition, enhancement of the RLMPSO model is introduced by incorporating additional search operations. The enhanced RLMPSO model (i.e. ERLMPSO) comprises three layers, namely, a global layer with four search operations, a local layer with one search operation, and a component-based layer with twelve search operations. Finally, the proposed two-stage ERLMPSOFSVM model is applied to a real-world Malaysian vehicle license plate recognition (VLPR) task. A high recognition rate of 98.1% has been achieved, confirming the effectiveness of the proposed two-stage ERLMPSO-FSVM model in tackling the license plate recognition problem

Bio-inspired computation: where we stand and what's next

In recent years, the research community has witnessed an explosion of literature dealing with the adaptation of behavioral patterns and social phenomena observed in nature towards efficiently solving complex computational tasks. This trend has been especially dramatic in what relates to optimization problems, mainly due to the unprecedented complexity of problem instances, arising from a diverse spectrum of domains such as transportation, logistics, energy, climate, social networks, health and industry 4.0, among many others. Notwithstanding this upsurge of activity, research in this vibrant topic should be steered towards certain areas that, despite their eventual value and impact on the field of bio-inspired computation, still remain insufficiently explored to date. The main purpose of this paper is to outline the state of the art and to identify open challenges concerning the most relevant areas within bio-inspired optimization. An analysis and discussion are also carried out over the general trajectory followed in recent years by the community working in this field, thereby highlighting the need for reaching a consensus and joining forces towards achieving valuable insights into the understanding of this family of optimization techniques

Evolutionary Computation, Optimization and Learning Algorithms for Data Science

A large number of engineering, science and computational problems have yet to be solved in a computationally efficient way. One of the emerging challenges is how evolving technologies grow towards autonomy and intelligent decision making. This leads to collection of large amounts of data from various sensing and measurement technologies, e.g., cameras, smart phones, health sensors, smart electricity meters, and environment sensors. Hence, it is imperative to develop efficient algorithms for generation, analysis, classification, and illustration of data. Meanwhile, data is structured purposefully through different representations, such as large-scale networks and graphs. We focus on data science as a crucial area, specifically focusing on a curse of dimensionality (CoD) which is due to the large amount of generated/sensed/collected data. This motivates researchers to think about optimization and to apply nature-inspired algorithms, such as evolutionary algorithms (EAs) to solve optimization problems. Although these algorithms look un-deterministic, they are robust enough to reach an optimal solution. Researchers do not adopt evolutionary algorithms unless they face a problem which is suffering from placement in local optimal solution, rather than global optimal solution. In this chapter, we first develop a clear and formal definition of the CoD problem, next we focus on feature extraction techniques and categories, then we provide a general overview of meta-heuristic algorithms, its terminology, and desirable properties of evolutionary algorithms

Bio-inspired computation: where we stand and what's next

In recent years, the research community has witnessed an explosion of literature dealing with the adaptation of behavioral patterns and social phenomena observed in nature towards efficiently solving complex computational tasks. This trend has been especially dramatic in what relates to optimization problems, mainly due to the unprecedented complexity of problem instances, arising from a diverse spectrum of domains such as transportation, logistics, energy, climate, social networks, health and industry 4.0, among many others. Notwithstanding this upsurge of activity, research in this vibrant topic should be steered towards certain areas that, despite their eventual value and impact on the field of bio-inspired computation, still remain insufficiently explored to date. The main purpose of this paper is to outline the state of the art and to identify open challenges concerning the most relevant areas within bio-inspired optimization. An analysis and discussion are also carried out over the general trajectory followed in recent years by the community working in this field, thereby highlighting the need for reaching a consensus and joining forces towards achieving valuable insights into the understanding of this family of optimization techniques