Apu Kumar Saha

National Institute of Technology Agartala · Department of Mathematics

This paper introduces a novel metaheuristic technique, the Greater Cane Rat Algorithm (GCRA), for solving optimization problems. GCRA's optimization process is inspired by the intelligent foraging behaviours of greater cane rats during and outside the mating season. These nocturnal animals leave trails as they forage through reeds and grass, which...

A R T I C L E I N F O Keywords: Centroid opposition-based learning (CODL) Backtracking search algorithm Multiple learning Chaos elite strategy Engineering design problem A B S T R A C T Evolutionary algorithms (EAs) have a lot of potential to handle nonlinear and non-convex objective functions. Particularly, the backtracking search algorithm (BSA)...

The comfort of the local people and the sustainable growth of urban areas are strongly linked to the public health of that area, and sustainable urban drainage systems (SUDS) are an essential component of both. The process of figuring out how to make SUDS better is essential to its development. The best SUDS method for guaranteeing sustainable deve...

This paper introduces the SIR Optimizer (SIRO), a novel next-generation learned metaheuristic algorithm inspired by biological systems and deep learning techniques. The optimizer uses the susceptible-infected-removed (SIR) epidemiological model to predict the population’s susceptibility, active infections, and recoveries. To enhance the search proc...

Increasing production of waste materials from the healthcare sector is a burning problem, causing health hazards for living beings and the environment. Thus, it is pertinent to identify a proper healthcare waste treatment technology for public health and environmental safety. Healthcare waste recycling (HCWR) is a kind of HCW management that could...

Expanding the capacity of optimization algorithms for simultaneous optimization of multiple competing objectives is a crucial aspect of research. This study presents MnMOMFO, a novel non-dominated sorting (NDS) and crowding distance (CD)-based multi-objective variant of the moth-flame optimization (MFO) algorithm for multi-objective optimization pr...

The selection of Ph.D (Doctor of Philosophy) supervisor is always a vital and interesting problem in academia and especially for students who want to carry out Ph.D. Nowadays, selecting a supervisor for Ph.D in a scientific manner becomes a challenge for any student because of the variety of options available to the scholar. In this context, the pr...

The Whale Optimization Algorithm (WOA) had widespread use across a wide variety of scientific and engineering domains for its simple and efficient behavior. However, it has some deficiencies, like slow convergence, faltering at local optima and not good at stability. To tackle these deficiencies, an improved variant of WOA, called F-WOA is introduc...

The Whale Optimization Algorithm (WOA), while its easy and efficient behavior, has a constrained capacity for exploration, which precludes it from discovering the global optima of a variety of problems. In addition, WOA has poor solution accuracy and sluggish convergence rate. To overcome these flaws of WOA, in this study, an improved WOA, called R...

For the simultaneous optimization of many conflicting objectives, the capability of optimization algorithms must be increased. In this research, a non-dominated sorting (NDS) and crowding distance (CD)-based multi-objective variant of an advanced moth flame optimization (MFO) has been offered. Firstly, a mathematical quasi-reflection-based learning...

Andrews and Newman introduced the mex-function \(\text {mex}_{A,a}(\lambda )\) for an integer partition \(\lambda \) of a positive integer n as the smallest positive integer congruent to a modulo A that is not a part of \(\lambda \). They then defined \(p_{A,a}(n)\) to be the number of partitions \(\lambda \) of n satisfying \(\text {mex}_{A,a}(\la...

The Butterfly Optimization Algorithm (BOA) is used widely in the field of optimization due to its proven effectiveness. However, the algorithm has certain limitations such as poor exploration-exploitation balance, lack of diversity, and entrapment into local solutions. To overcome these limitations, a new and improved modification of BOA, called LQ...

The moth flame optimization (MFO) algorithm is a swarm intelligence (SI) based algorithm which gained popularity among researchers due to a special kind of movement mechanism, namely, a transverse orientation mechanism of the moth in nature. Like other SI based algorithms, it also suffers from good quality solution and slow convergence speed. To av...

Abstract: This study introduces an innovative metaheuristic algorithm, dubbed the Fire Hawk Optimization (FHO), influenced by the hunting techniques of Fire Hawks, including whistling kites, black kites, and brown falcons, which exhibit unique hunting methods, involving the use of fire to catch prey. But it has some limitations, such as its inabili...

Single candidate optimizer (SCO) is a new member of metaheuristic (MH) algorithm based on a single candidate solution throughout the whole optimization scheme. To balance between two major factor of MH algorithm i.e., diversification and intensification, SCO utilized a unique set of solutions which helps effectively in updating the position of the...

The COVID-19 is one of the most significant obstacles that humanity is now facing. The use of computed tomography (CT) images is one method that can be utilized to recognize COVID-19 in early stage. In this study, an upgraded variant of Moth flame optimization algorithm (Es-MFO) is presented by considering a nonlinear self-adaptive parameter and a...

Convergence analysis of any random search algorithm helps to verify whether and how quickly the algorithm guarantees convergence to the point of interest. Butterfly optimization algorithm (BOA) is a popular population-based stochastic optimizer introduced by mimicking the foraging behaviors of butterflies in nature. In this paper, we have developed...

Moth Flame Optimization (MFO) is a nature-inspired optimization algorithm, based on the principle of navigation technique of moth toward moon. Due to less parameter and easy implementation, MFO is used in various field to solve optimization problems. Further, for the complex higher dimensional problems, MFO is unable to make a good trade-off betwee...

The whale optimization algorithm (WOA), a biologically inspired optimization technique, is known for its straightforward design and effectiveness. Despite many advantages, it has certain disadvantages, such as a limited exploration capacity and early convergence as a result of the minimal exploration of the search process. The WOA cannot bypass the...

Abstract: The Whale Optimization Algorithm (WOA) has been extensively utilized in numerous scientific and engineering fields for its simple and efficient behavior. However, it has some deficiencies, like slow convergence, stagnating at local optima and poor stability. To tackle these deficiencies, an improved variant of WOA, called QFWOA is introdu...

Fermatean fuzzy set (FFS) is an expedient tool in today’s world for coping with uncertainty, ambiguity, and incompleteness that emerge in many decision-making processes. In light of the relevance of FFS, this work offers a Multi-Criteria Group Decision Making (MCGDM) method together with Bonferroni mean and weighted Bonferroni mean operators in FF...

Abstract: Moth flame optimization (MFO) algorithm is a recent nature-inspired algorithm, based on the mechanism called transverse orientation. This mechanism contains a special type of navigation techniques, which represents the movement of moths towards moon in a straight path at night. MFO has been successfully applied on several optimization p...

The local optima stagnation is a major issue with all meta-heuristic algorithms. In this paper, a hybrid slime mould algorithm (SMA) is proposed with the aid of quadratic approximation to address the aforesaid problem to expedite the explorative strength of slime mould in nature. As quadratic approximation performs better within the local confineme...

This paper proposes an improvement to the dwarf mongoose optimization (DMO) algorithm called the advanced dwarf mongoose optimization (ADMO) algorithm. The improvement goal is to solve the low convergence rate limitation of the DMO. This situation arises when the initial solutions are close to the optimal global solution; the subsequent value of th...

Recycling the essential components of lithium ion batteries (LIBs) has become more important than ever because these batteries include combustible and hazardous elements. At the same time, recovery of major components from LIBs might provide some economic benefits. The goal of this paper is to utilize a multi-criteria group decision making (MCGDM)...

Differential evolution (DE) is one of the highly acknowledged population-based optimization algorithms due to its simplicity, user-friendliness, resilience, and capacity to solve problems. DE has grown steadily since its beginnings due to its ability to solve various issues in academics and industry. Different mutation techniques and parameter choi...

This paper uses the Butterfly Optimization Algorithm (BOA) with dominated sorting and crowding distance mechanisms to solve multi-objective optimization problems. There is also an improvement to the original version of BOA to alleviate its drawbacks before extending it into a multi-objective version. Due to better coverage and a well-distributed Pa...

In material handling, warehousing, manufacturing and construction applications, forklifts are vital equipment, which are used to engage, lift and move palletized items. So, selection of the most appropriate forklift is an essential task for transportation of materials in warehouses for optimal use of the equipment. The present treatise introduces a...

Due to the synergic interaction of matrix and reinforcement, the workability of polymeric composite materials has shown its dependency on compositions. In the present study, Weighted Aggregate Sum Product Assessment (WASPAS) has been applied for ranking the developed sustainable polymeric composite materials. The different properties of composite m...

The Moth flame optimization (MFO) algorithm belongs to the swarm intelligence family and is applied to solve complex real-world optimization problems in numerous domains. MFO and its variants are easy to understand and simple to operate. However, these algorithms have successfully solved optimization problems in different areas such as power and en...

Moth flame optimization (MFO) algorithm is a relatively new nature-inspired optimization algorithm based on the moth’s movement towards the moon. Premature convergence and convergence to local optima are the main demerits of the algorithm. To avoid these drawbacks, a modified dynamic opposite learning-based MFO algorithm (m-DMFO) is presented in th...

This paper proposes a hybrid sine cosine butterfly optimization algorithm (m-SCBOA), in which a modified butterfly optimization algorithm is combined with sine cosine algorithm to achieve superior exploratory and exploitative search capabilities. The newly suggested m-SCBOA algorithm has been tested on 39 benchmark functions and compared with seven...

A retaining wall is a structure used to resist the lateral pressure of soil or any backfill material. Cantilever retaining walls provide resistance to overturning and sliding by using backfill weight. In this paper, the weight and cost of the cantilever retaining wall have been minimized using a hybrid metaheuristic optimization technique, namely,...

Whale optimization algorithm (WOA) has been developed based on the hunting behavior of humpback whales. Though it has a considerable convergence speed, WOA suffers from diversity in the solution due to the low exploration of search space. As a result, it tends to trap in local optima and suffer from low solution accuracy. This study proposes a nove...

The Moth Flame Optimization (MFO) algorithm shows decent performance results compared to other meta-heuristic algorithms for tackling non-linear constrained global optimization problems. However, it still suffers from obtaining quality solution and slow convergence speed. On the other hand, the Butterfly Optimization Algorithm (BOA) is a comparativ...

Migration from a linear to a circular economy (CE) has become inevitable to reduce waste by making reusable products and materials. The present health care waste (HCW) management development has also been reformed due to this change. Since there is a strong connection between HCW management and CE, in this study, the HCW recycling technology select...

Because of their superior problem-solving ability, nature-inspired optimization algorithms are being regularly used in solving complex real-world optimization problems. Engineering academics have recently focused on meta-heuristic algorithms to solve various optimization challenges. Among the state-of-the-art algorithms, Differential Evolution (DE)...

Moth flame optimization (MFO) algorithm is a relatively new nature-inspired optimization algorithm based on the moth’s movement towards the moon. Premature convergence and convergence to local optima are the main demerits of the basic MFO algorithm. To avoid these drawbacks, a new variant of MFO algorithm, namely a modernized MFO (M-MFO) algorithm...

Moth flame optimization (MFO) algorithm is a relatively new nature-inspired optimization algorithm based on the moth's movement towards the moon. Premature convergence and convergence to local optima are the main demerits of the basic MFO algorithm. To avoid these drawbacks, a new variant of MFO algorithm, namely a modernized MFO (M-MFO) algorithm...

An intuitionistic fuzzy random variable (IFRV) handles ambiguous, incomplete and ill-known data or information along with statistical variability, and deals with fuzzy number, grade of membership and non-membership functions and probability distribution function. So, taking such advantages of IFRV, we extend the classical continuous review inventor...

The sine cosine algorithm (SCA) is a population-based metaheuristic strategy that has been demonstrated competitive performance and has received significant attention from scientists in various fields. Regardless, like other population-based techniques, SCA also has a tendency to get stuck in adjacent optima and uneven exploitation. Given the short...

Deep learning (DL) models are becoming pervasive and applicable to computer vision, image processing, and synthesis problems. The performance of these models is often improved through architectural configuration, tweaks, the use of enormous training data, and skillful selection of hyperparameters. The application of deep learning models to medical...

The exposition of any nature-inspired optimization technique relies firmly upon its executed organized framework. Since the regularly utilized backtracking search algorithm (BSA) is a fixed framework, it is not always appropriate for all difficulty levels of problems and, in this manner, probably does not search the entire search space proficiently...

Nature-inspired meta-heuristics have demonstrated superior efficiency in the solution of complicated nonlinear optimization problems than conventional techniques. In this article, an enhanced moth flame optimization (EMFO) is designed using the mutualism phase from the symbiotic organism search (SOS) algorithm. The suggested approach is examined on...

Quantum-dot cellular automata (QCA) is field-coupled nanotechnology that achieves high device density, high switching speed, and low power dissipation. The flow of information within QCA is fully controlled by the position of the electrons only. A proper cell placement and its respective clock zone for the correct logic operation become the utmost...

Backtracking search algorithm (BSA) is a nature-based optimization technique extensively used to solve various real-world global optimization problems for the past few years. The present work aims to introduce an improved BSA (ImBSA) based on a multi-population approach and modified control parameter settings to apprehend an ensemble of various mut...

Though the Butterfly Bptimization Algorithm (BOA) has already proved its effectiveness as a robust optimization algorithm, it has certain disadvantages. So, a new variant of BOA, namely mLBOA, is proposed here to improve its performance. The proposed algorithm employs a self-adaptive parameter setting, Lagrange interpolation formula, and a new loca...

Quantum-dot cellular automata (QCA) has gained an exclusive research focus on the current CMOS due to low power dissipation, less circuit area occupancy, and computational speed. The effectiveness of a QCA circuit is affected by the primary logic (majority vote and inverter). Hence, the minimization of these primitives for QCA logic circuit synthes...

The operational performance of hydropower plants (HPPs) is largely affected as the output from the plant entirely depends on the rainfall and demand from consumers both of which are compromised due to the vulnerability in climatic patterns and rapid change in urbanization rate. Although, not all the parameters are equally affected and the present s...

Because of its importance and numerous applications, the selection of the best supplier from a pool of accessible suppliers is gaining importance across the world. To reduce the cost of supply chain and to improve the quality of the products, selection of a relevant supplier has become one of the crucial decisions. The objective of this study is to...

Abstract: The combined economic and emission dispatch (CEED) problem is a highly non-linear multi-objective problem with equality and inequality constraints and is considered as crucial task in operation and planning of power systems problem. Due to the conflicting nature of the objectives of CEED, it is popular among the researchers. On the other...

A new pandemic disease named as novel corona virus disease (COVID-19) was discovered during end of 2019 in Wuhan city of China and was quickly spread throughout the globe. But, till now no medicine is available to fight against the infection caused by the disease. The infection may also be transmitted easily from person to person through highly inf...

The symbiotic organisms search (SOS) algorithm was introduced by considering the relationships among the creatures in a natural ecosystem. Despite the superior efficiency of SOS, it has been observed that fixing benefit factors of mutualism phase at 1 or 2; the algorithm obstructs itself from an extensive and diverse search of the search region. Mo...

Coronavirus disease 2019 (COVID-19) has caused a massive disaster in every human life field, including health, education, economics, and tourism, over the last year and a half. Rapid interpretation of COVID-19 patients' X-ray images is critical for diagnosis and, consequently, treatment of the disease. The major goal of this research is to develop...

The significant physical challenges of Complementary Metal Oxide Semiconductor (CMOS) technology drives it on the brink of ultimate limit. With the surfacing of tremendous research findings, Quantum dot cellular automata (QCA) has emerged as a new technology in nanoscale era as a viable alternative to CMOS technology. Besides its wide acceptance, Q...

The search for food stimulated by hunger is a common phenomenon in the animal world. Mimicking the concept, recently, an optimization algorithm Hunger Games Search (HGS) has been proposed for global optimization. On the other side, the Whale Optimization Algorithm (WOA) is a commonly utilized nature-inspired algorithm portrayed by a straightforward...

Differential evolution and its variants have already proven their worth in the field of evolutionary optimization techniques. This study further enhances the success history-based adaptive differential evolution (SHADE) by hybridizing it with a modified Whale optimization algorithm (WOA). In the new algorithm, the two algorithms, SHADE and modified...

Quantum dot Cellular Automata (QCA) are a prominent nanotechnology that is widely employed in digital circuits and systems. In comparison to complementary metal–oxide semiconductor (CMOS) technology, QCA is a remarkable and challenging alternative with many attractive aspects such as fast execution and low power use. To accomplish all arithmetic pr...

Whale optimization algorithm was developed based on the prey-catching characteristics of the humpback whales. Due to its simple structure and efficiency, the researchers employed the algorithm to address numerous disciplines’ numerous problems. The profound analysis of the whale optimization algorithm discloses that the algorithm suffers from low e...

The transistor-based CMOS technology is facing tremendous physical challenges in nano-scale design. The Quantum-dot cellular automata (QCA) have attracted the research focus as a prospective viable alternative to CMOS technology for future designs in the nano-scale. The cell interaction property helps in information propagation in QCA. The 3-input...

The preparedness of Indian states and union territories (UTs) against the COVID‐19 pandemic has been evaluated. Ten parameters related to demographic, socioeconomic, and healthcare aspects have been considered and the performances of 27 states and three UTs have been evaluated applying the Fuzzy Analytic Hierarchy Process. Opinions of medical exper...

Convergence analysis of any random search algorithm helps to verify whether and how quickly the algorithm guarantees convergence to the point of interest. Butterfly optimization algorithm (BOA) is a popular population-based stochastic optimizer introduced by mimicking the foraging behaviors of butterflies in nature. In this paper, we have developed...

The present CMOS VLSI technology is facing some challenges like working in nano scale, device density, power dissipation, operating frequency, fast execution, which demands a proper alternative. Quantum dot Cellular Automata (QCA) is one of the feasible substitutes for the same. In QCA, clocking is the primary driving source of power, and the flow...

Notwithstanding the superior performance of the Whale optimization algorithm (WOA) on a wide range of optimization issues, the exploitation in WOA gets more preference during the search process, thereby compromising the solution accuracy and diversity and also increases the chance of premature convergence. In this study, a novel modified WOA (m-SDW...