Suash Deb

CVRCE, India · Computer Science & Engg

Over the past few decades, the application of iterated function systems (IFS) in reconstructing fractal images has been a challenging research area. Numerous methods have been proposed to address this issue. However, they generally focus on binary or grayscale images, neglecting the color component of the process. Consequently, they are unsuitable...

Computational Intelligence methods for automatic generation of sport training plans in individual sport disciplines have achieved a mature phase. In order to confirm their added value, they have been deployed into practice. As a result, several methods have been developed for generating well formulated training plans on computers automatically that...

The authors got the motivation for writing the paper based on an issue, with which developers of the newly developed nature-inspired algorithms are usually confronted today: How to select the test benchmark such that it highlights the quality of the developed algorithm most fairly? In line with this, the CEC Competitions on Real-Parameter Single-Ob...

Swarm intelligence (SI) is the collective behavior of decentralized, self-organized natural or artificial systems. Monarch butterfly optimization (MBO) algorithm is a class of swarm intelligence metaheuristic algorithm inspired by the migration behavior of monarch butterflies. Through the migration operation and butterfly adjusting operation, indiv...

Advising athletes how to improve their performance after a race is a very important aspect of sport training. It can also be called a post-hoc analysis, which often includes a deep analysis of an athlete's performance, behavior and body characteristics after a race. These analyses help trainers to adapt their training plan according to the athlete'...

Preference time in a triathlon denotes the time that is planned to be achieved by an athlete in a particular competition. Usually, the preference time is calculated some days, weeks, or even months before the competition. Mostly, trainers calculate the proposed preference time according to the current form, body performances of athletes, psychologi...

Nowadays, the majority of data on the Internet is held in an unstructured format, like websites and e-mails. The importance of analyzing these data has been growing day by day. Similar to data mining on structured data, text mining methods for handling unstructured data have also received increasing attention from the research community. The paper...

Many optimization problems in science and engineering are highly nonlinear, and thus require sophisticated optimization techniques to solve. Traditional techniques such as gradient-based algorithms are mostly local search methods, and often struggle to cope with such challenging optimization problems. Recent trends tend to use nature-inspired optim...

In this chapter, major contributions of swarm intelligence in the fields of logistics and production planning are discussed. Starting with a general introduction to planning problems in these fields, we outline the limitations of traditional optimization approaches and the reasons for using methods from the field of swarm intelligence such as the N...

Recently, by examining and simulating the migration behavior of monarch butterflies in nature, Wang et al. proposed a new swarm intelligence-based metaheuristic algorithm, called monarch butterfly optimization (MBO), for addressing various global optimization tasks. The effectiveness of MBO was verified by benchmark evaluation on an array of unimod...

Recently many researchers invented a wide variety of meta-heuristic optimization algorithms. Most of them achieved remarkable performance results by infusing the natural phenomena or biological behaviors into the search logics of the optimization algorithms, such as PSO, Cuckoo Search and so on. Although these algorithms have promising performance,...

Elephant search algorithm (ESA) is one of the contemporary meta-heuristic search algorithms recently proposed. The male elephants are responsible for global exploration, roaming to new dimensions of search space. The female elephants focus on doing local search, for finding the optimal solution. A lifespan mechanism is designed to control the birth...

Many optimization problems in science and engineering are challenging to solve, and the current trend is to use swarm intelligence (SI) and SI-based algorithms to tackle such challenging problems. Some significant developments have been made in recent years, though there are still many open problems in this area. This paper provides a short but tim...

In this paper, a novel metaheuristic search algorithm inspired by rhinoceros' natural behaviour is proposed, namely Rhinoceros Search Algorithm (RSA). Similar to our earlier version called Elephant Search Algorithm, RSA simplifies certain habitual characteristics and stream line the search operations, thereby reducing the number of operational para...

Data clustering is one of the most popular branches of machine learning and data analysis. Partitioning-based type of clustering algorithms, such as K-means, is prone to the problem of producing a set of clusters that is far from perfect due to its probabilistic nature. The clustering process starts with some random partitions at the beginning, and...

A variant of particle swarm optimization (PSO) is represented to solve the infinitive impulse response (IIR) system identification problem. Called improved PSO (IPSO), it makes significant enhancement over PSO. To begin with, the population initialization step makes use of golden ratio to segment solution space so as to obtain high-quality solution...

Feed-forward neural networks are efficient at solving various types of problems. However, finding efficient training algorithms for feed-forward neural networks is challenging. The dynamic group optimisation (DGO) algorithm is a recently proposed half-swarm half-evolutionary algorithm, which exhibits a rapid convergence rate and good performance in...

In this paper, we propose ParFeatArch Generator, a new algorithm for generating Neural Network architectures with optimal features and parameters through Particle Swarm Optimization. Selecting the best architecture for a Neural Network is usually done through a trial and error process, in which the number of layers is selected usually based on prev...

Data clustering is one of the most popular branches of machine learning and data analysis. Partitioning-based type of clustering algorithms, such as K-means, is prone to the problem of producing a set of clusters that is far from perfect due to its probabilistic nature. The clustering process starts with some random partitions at the beginning, and...

Nowadays, swarm intelligence algorithms are becoming increasingly popular for solving many optimization problems. The Wolf Search Algorithm (WSA) is a contemporary semi-swarm intelligence algorithm designed to solve complex optimization problems and demonstrated its capability especially for large-scale problems. However, it still inherits a common...

Deep learning (DL) is one of the most emerging types of contemporary machine learning techniques that mimic the cognitive patterns of animal visual cortex to learn the new abstract features automatically by deep and hierarchical layers. DL is believed to be a suitable tool so far for extracting insights from very huge volume of so-called big data....

Earthworms can aerate the soil with their burrowing action and enrich the soil with their waste nutrients. Inspired by the earthworm contribution in nature, a new kind of bio-inspired metaheuristic algorithm, called earthworm optimisation algorithm (EWA), is proposed in this paper. The EWA method is inspired by the two kinds of reproduction (Reprod...

Since the development of cuckoo search (CS) by Yang and Deb in 2009, CS has been applied in a diverse range of applications. This paper first outlines the key features of the algorithm and its variants, and then briefly summarizes the state-of-the-art developments in many applications. The opportunities for further research are also identified.

Modeling preference time in triathlons means predicting the intermediate times of particular sports disciplines by a given overall finish time in a specific triathlon course for the athlete with the known personal best result. This is a hard task for athletes and sport trainers due to a lot of different factors that need to be taken into account, e...

This paper presents a novel binary monarch butterfly optimization (BMBO) method, intended for addressing the 0–1 knapsack problem (0–1 KP). Two tuples, consisting of real-valued vectors and binary vectors, are used to represent the monarch butterfly individuals in BMBO. Real-valued vectors constitute the search space, whereas binary vectors form th...

This paper introduces a novel framework for addressing multi-objective optimization problems (MOPs) by means of “multi-objective fruit fly optimization algorithm” (MOFOA). The proposed algorithm is applied for optimal shape design of tubular linear synchronous motor (TLSM). The corresponding objective functions aims to maximize operating force and...

To predict the final result of an athlete in a marathon run thoroughly is the eternal desire of each trainer. Usually, the achieved result is weaker than the predicted one due to the objective (e.g., environmental conditions) as well as subjective factors (e.g., athlete's malaise). Therefore, making up for the deficit between predicted and achieved...

A novel bio-inspired optimization algorithm called elephant search algorithm (ESA) has been applied to solve NP-hard problems including the classical traveling salesman problem (TS) in this paper. ESA emerges from the hybridization of evolutionary mechanism and dual balancing of exploitation and exploration. The design of ESA is inspired by the beh...

Nature has provided rich models for computational problem solving, including optimizations based on the swarm intelligence exhibited by fireflies, bats, and ants. These models can stimulate computer scientists to think nontraditionally in creating tools to address application design challenges.

This study proposes a novel chaotic cuckoo search (CCS) optimization method by incorporating chaotic theory into cuckoo search (CS) algorithm. In CCS, chaos characteristics are combined with the CS with the intention of further enhancing its performance. Further, the elitism scheme is incorporated into CCS to preserve the best cuckoos. In CCS metho...

Recently many researchers invented a wide variety of meta-heuristic optimization algorithms and they have achieved remarkable performance results. Through observing natural phenomena, clues were inspired and programmed into search logics, such as PSO, Cuckoo Search and so on. Although those algorithms have promising performance, there still exist a...

A novel hybrid Krill herd (KH) and quantum-behaved particle swarm optimization (QPSO), called KH-QPSO, is presented for benchmark and engineering optimization. QPSO is intended for enhancing the ability of the local search and increasing the individual diversity in the population. KH-QPSO is capable of avoiding the premature convergence and eventua...

In order to fully exploit global search and local search ability of krill herd (KH), a multi-stage krill herd (MSKH) algorithm is presented to solve global numerical optimization problem. The proposed method involves exploration and exploitation stage. The exploration stage uses a basic krill herd (KH) algorithm to select a good candidate solution...

A recently proposed metaheuristics called Wolf Search Algorithm (WSA) has demonstrated its efficacy for various hard-to-solve optimization problems. In this paper, an improved version of WSA namely Eidetic-WSA with a global memory structure (GMS) or just eWSA is presented. eWSA makes use of GMS for improving its search for the optimal fitness value...

In this paper, a new swarm-based metaheuristic algorithm, called elephant herding optimisation EHO, is proposed for solving global optimisation tasks, which is inspired by the herding behaviour of the elephant groups. In nature, the elephants belonging to different clans live together under the leadership of a matriarch, and the male elephants will...

In this paper, a new kind of swarm-based metaheuristic search method, called Elephant Herding Optimization (EHO), is proposed for solving global optimization tasks. EHO is inspired by the herding behavior of elephant group. These behaviors can be modelled into two following operators: clan updating operator and separating operator. In EHO, the elep...

This paper proposes an improved particle swarm optimization (IPSO) algorithm for IIR system identification problem. IPSO adopts three novel steps as follows: The population initialization step is based on golden ratio, which is beneficial for improving the quality of candidate solutions. In velocity updating step, all particles use different inerti...

Recently, inspired by migration of monarch butterflies in the Northern American, a new kind of metaheuristic algorithm, called monarch butterfly optimization (MBO), is proposed for solving global optimization problems. It has been experimentally shown that MBO outperforms five state-of-the-art metaheuristic algorithms on most benchmarks. However, t...

When krill herd (KH) is used to solve complicated multimodal functions, sometimes it fails to find the best solutions and cannot converge fast. Herein, we propose a hybrid KH method, called PBILKH, by integrating the KH with the population-based incremental learning (PBIL). In addition, a type of elitism is applied to memorize the krill with the be...

In this paper, a new kind of swarm-based metaheuristic search method, called Elephant Herding Optimization (EHO), is proposed for solving optimization tasks. The EHO method is inspired by the herding behavior of elephant group. In nature, the elephants belonging to different clans live together under the leadership of a matriarch, and the male elep...

With the rapid development of science and technology, we are enjoying the convenience of the digital information technology. Virtual Reality technology is the product of digital information technology, which applies computer simulation to generate a three-dimensional (3D) virtual space. It provides not only users with experiences of visual, auditor...

A novel bio-inspired optimization algorithm called Elephant Search Algorithm (ESA) is proposed in this paper. ESA emerges from the hybridization of evolutionary mechanism and dual balancing of exploitation and exploration. The design of ESA is inspired by the behavioral characteristics of elephant herds; hence the name Elephant Search Algorithm whi...

Earthworms are essential animals that aerate the soil with their burrowing action and enrich the soil with their waste nutrients. Inspired by the earthworm contribution in nature, a new kind of bio-inspired metaheuristic algorithm, called earthworm optimization algorithm (EWA), is proposed in this paper. The EWA method is inspired by the two kinds...

Krill herd (KH) algorithm is a novel swarm-based approach which mimics the herding and foraging behavior of krill species in sea. In our current work, KH method is discretized and incorporated into some heuristic strategies so as to form an effective approach, called discrete krill herd (DKH). The intention has been to use DKH towards solving the f...

In nature, the eastern North American monarch population is known for its southward migration during the late summer/autumn from the northern United States and southern Canada to Mexico, covering thousands of miles. By simplifying and idealizing the migration of monarch butterflies, a new kind of nature-inspired metaheuristic algorithm, called Mona...

Modern metaheuristic algorithms are in general suited for global optimization. This paper combines the recently developed eagle strategy algorithm with differential evolution. The new algorithm, denoted as the ES–DE, is implemented by interfacing SAP2000 structural analysis code and MATLAB mathematical software. The performance of the ES–DE is eval...

Nature-inspired algorithms usually use some form of attraction and diffusion as a mechanism for exploitation and exploration. In this paper, we investigate the role of attraction and diffusion in algorithms and their ways in controlling the behaviour and performance of nature-inspired algorithms. We highlight different ways of implementation of att...

Metaheuristics have a substantial history in fine-tuning machine learning algorithms. They gained tremendous popularity in many application domains. Robotics on the other hand is a wide research discipline that embraces artificial intelligence in a complex individually-thinking robot and distributed robots. Recently, metaheuristics made a significa...

Optimization problems can become intractable when the search space undergoes tremendous growth. Heuristic optimization methods have therefore been created that can search the very large spaces of candidate solutions. These methods, also called metaheuristics, are the general skeletons of algorithms that can be modified and extended to suit a wide r...

Nature-inspired algorithms usually use some form of attraction and diffusion as a mechanism for exploitation and exploration. In this paper, we investigate the role of attraction and diffusion in algorithms and their ways in controlling the behaviour and performance of nature-inspired algorithms. We highlight different ways of implementation of att...

Nature-inspired algorithms usually use some form of attraction and diffusion as a mechanism for exploitation and exploration. In this paper, we investigate the role of attraction and diffusion in algorithms and their ways in controlling the behaviour and performance of nature-inspired algorithms. We highlight different ways of implementation of att...

Firefly algorithm (FA) is a new member of bionic swarm intelligence optimization algorithms. In this paper the bionic principle of FA is investigated and extended to solving a well-known NP-hard problem: Permutation Flow-Shop Scheduling Problem (PFSP). A corresponding solution strategy for PFSP bound is designed with its scheduling features. The st...

This study proposes a novel chaotic cuckoo search (CCS) optimization method by introducing chaotic theory into cuckoo search (CS) algorithm. In CCS, chaos characteristics are combined with the CS with the intention of further enhancing its performance. Further, the elitism scheme is incorporated into CCS in order to preserve the best cuckoos. In th...

In this paper, the design of an asynchronized messaging system where a user may proactively seek for answers or advice by depositing a question on the messaging system is presented. The messaging system will automatically disseminate the question which is related to a specific location, to a group of users who are either within the proximity curren...

Law enforcement officers nowadays are usually equipped with some camera device which captures real time visuals on their duties. The footages would be used as evidence and they carry legal effects. However in the near future, civilians are encouraged to use their smart phone in recording footages in critical scenes; how could the video or pictures...

The efficiency of any metaheuristic algorithm largely depends on the way of balancing local intensive exploitation and global diverse exploration. Studies show that bat algorithm can provide a good balance between these two key components with superior efficiency. In this paper, we first review some commonly used metaheuristic algorithms, and then...

Traditional K-means clustering algorithms have the drawback of getting stuck at local optima that depend on the random values of initial centroids. Optimization algorithms have their advantages in guiding iterative computation to search for global optima while avoiding local optima. The algorithms help speed up the clustering process by converging...

The purpose of classification in medical informatics is to predict the presence or absence of a particular disease as well as disease types from historical data. Medical data often contain irrelevant features and noise, and an appropriate subset of the significant features can improve classification accuracy. Therefore, researchers apply feature se...

Sonar signals recognition is an important task in detecting the presence of some significant objects under the sea. In military, sonar signals are used in lieu of visuals to navigate underwater and/or locate enemy submarines in proximity. In particular, classification algorithm in data mining has been applied in sonar signal recognition for recogni...

Outlier detection is one of the most important data mining techniques. It has broad applications like fraud detection, credit approval, computer network intrusion detection, anti-money laundering, etc. The basis of outlier detection is to identify data points which are “different” or “far away” from the rest of the data points in the given dataset....

It has become a prevalent lifestyle nowadays that netizens voice their opinions on social networks (Web 2.0), for matters of all sizes, and on a regular basis. The opinions which initially should be intended for their groups of friends propagate to all public users. This pond of opinions in the forms of forum posts, messages written on micro-blogs,...