Nature-inspired Algorithms Research Articles

Electromagnetic Absorbing Materials Design by Optimization Using a Machine Learning Approach

One of the state-of-the-art strategies to decrease the isolation between sections of personal digital assistance devices, taking into account the unavoidable thermal, mechanical, weight, and manufacturability constraints is the proper introduction of electromagnetic (EM) absorbing materials. The global target of this study is the optimized synthesis of the EM properties of a material using a machine learning approach. The sought material must improve the shielding effectiveness between two regions of a device. The first part of this paper is dedicated to the assessment of the performances of an artificial neural network used as function approximation to compute the values of the shielding without resorting to complex and time-consuming full-wave numerical simulations. The network is assessed with respect to either the variations of the training set and algorithm or of its architecture. Significant figures of merit are introduced. The second part is devoted to the description and definition of nature-inspired algorithms demanded to manage the optimization problem.

On the performance of standard nature-inspired algorithms in solving non-stationary optimization problems

Non-stationary optimization problems are a very important class of problems in many practical applications. These problems are characterized by objective functions and constraints that change with time or environmental conditions, so the optimization solution also needs to be dynamically adjusted accordingly. Many algorithms from the field of evolutionary and biology inspired computation are known as an effective approach for dealing with hard optimization problems in changing environments, that is the result of modelling of self-organized systems in nature and evolution in the biology. Natural systems always exist in the changing environments. This article aims to compare the performance of three common nature-inspired techniques, namely genetic algorithm (GA), particle swarm optimization (PSO) algorithm, and differential evolution (DE) in their standard implementation when solving non-stationary optimization problems, so as to provide a reference and rationale for subsequent selection of appropriate algorithms and improvements.

Chicken swarm optimization modelling for cognitive radio networks using deep belief network-enabled spectrum sensing technique.

Cognitive radio networks (CRN) enable wireless devices to sense the radio spectrum, determine the frequency state channels, and reconfigure the communication variables to satisfy Quality of Service (QoS) needs by reducing energy utilization. In CRN, spectrum sensing is an essential process that is highly challenging and can be addressed by several traditional techniques, such as energy detection, match filtering, etc. For now, the current models' performance is impacted by the comparatively low Signal to Noise Ratio (SNR) of recognized signals and the insignificant quantity of traditional signal samples. This research proposals a new spectral sensing technique for cognitive radio networks (SST-CRN) that addresses the drawbacks of predictable energy detection models. With the use of a deep belief network (DBN), the suggested model contributes to accomplish a nonlinear threshold based on the chicken swarm algorithm (CSA). The proposed DBN enabled SST-CRN technique goes through two phases in a organized process: offline and online. Throughout the offline phase, the DBN model is methodically trained on pre-gathered data, developing the aptitude to identify problematic patterns and examples from the spectral features of the radio environment. This stage involves extensive feature extraction, validation, and model development to ensure that the DBN can professionally represent complicated spectral dynamics. Additionally, online spectrum sensing is conducted during the real communication phase to enable real-time adaptation to dynamic changes in the spectrum environment. Offline spectrum sensing is typically performed during a devoted sensing period before actual communication begins. When combined with DBN's deep learning capabilities and CSO's innate nature-inspired algorithms, a synergistic framework is created that enables CRNs to explore and allocate incidences on their own with astonishing accuracy. The proposed solution considerably improves the spectrum efficiency and resilience of CRNs by harnessing the power of DBN, which leads to more effective resource utilization and less interference. The Simulation results show that our proposed strategy produces more accurate spectrum occupancy assessments. The result parameters such as probability of detection, SNR of -24dB, the SST-CRN perfect has increased a developed Pd of 0.810, whereas the existing methods RMLSSCRN-100 and RMLSSCRN-300 have accomplished a lower Pd of 0.577 and 0.736, respectively. Our deep learning methodology uses convolutional neural networks to automatically learn and adapt to dynamic and complicated radio environments, improving accuracy and flexibility over classic spectrum sensing approaches. Future research might focus on improving CSO algorithms to better optimize the spectrum sensing process, enhancing the reliability of DBN-enabled sensing techniques.

Variable selection in Poisson regression model based on chaotic meta-heuristic search algorithm

By determining the most significant variables that are connected to the response variable, Increasing prediction accuracy and processing speed can be achieved through the process of variable selection. Regression modeling has drawn a lot of interest from several scientific domains. One of the most effective nature-inspired algorithms that has been suggested recently and can be used effectively for variable selection is the Firefly algorithm. The chaotic firefly algorithm is presented in this work to carry out the Poisson regression model's variable selection. A simulation study is carried out to assess how well the suggested strategy performs in terms of variable selection criteria and prediction accuracy. Its effectiveness is also contrasted with alternative approaches. The outcomes demonstrated the effectiveness of our suggested strategies, which beat other widely used approaches.

Nature inspired algorithms in dynamic task scheduling: A review

The process of scheduling involves allocating shared resources gradually so that tasks can be completed effectively within the allotted time. In task scheduling and resource allocation, the terms are used independently for tasks and resources, respectively. In computer science and operational management, scheduling is a hot topic. Efficient schedules guarantee system effectiveness, facilitate sound decision-making, reduce resource waste and expenses, and augment total productivity. Selecting the most accurate resources to complete work items and schedules for computing and business process execution is typically a laborious task. Particularly in dynamic real-world systems, where scheduling different dynamic tasks involves multiple tasks, is a difficult problem. Emerging technology known as "nature inspired algorithms" has the ability to dynamically solve the problem of optimal task and resource scheduling. This review paper discusses a study that looked at algorithms inspired by nature and used them to schedule tasks dynamically. The Nature Inspired Algorithms used in dynamic task scheduling and a comparative analysis of those methods are used in this paper to address the study's findings.

CaAIS: Cellular Automata-Based Artificial Immune System for Dynamic Environments

Artificial immune systems (AIS), as nature-inspired algorithms, have been developed to solve various types of problems, ranging from machine learning to optimization. This paper proposes a novel hybrid model of AIS that incorporates cellular automata (CA), known as the cellular automata-based artificial immune system (CaAIS), specifically designed for dynamic optimization problems where the environment changes over time. In the proposed model, antibodies, representing nominal solutions, are distributed across a cellular grid that corresponds to the search space. These antibodies generate hyper-mutation clones at different times by interacting with neighboring cells in parallel, thereby producing different solutions. Through local interactions between neighboring cells, near-best parameters and near-optimal solutions are propagated throughout the search space. Iteratively, in each cell and in parallel, the most effective antibodies are retained as memory. In contrast, weak antibodies are removed and replaced with new antibodies until stopping criteria are met. The CaAIS combines cellular automata computational power with AIS optimization capability. To evaluate the CaAIS performance, several experiments have been conducted on the Moving Peaks Benchmark. These experiments consider different configurations such as neighborhood size and re-randomization of antibodies. The simulation results statistically demonstrate the superiority of the CaAIS over other artificial immune system algorithms in most cases, particularly in dynamic environments.

F-TLBO-ID: Fuzzy fed teaching learning based optimisation algorithm to predict the number of [formula omitted]-barriers for intrusion detection

Ensuring fast and efficient Intrusion Detection and Prevention (IDP) at international borders is crucial for maintaining security and safeguarding nations. In this study, we propose an innovative approach that harnesses the power of machine learning and Wireless Sensor Networks (WSNs) to achieve faster and more accurate IDP. Our novel Fuzzy fed Teaching Learning Based Optimisation regression algorithm (F-TLBO-ID) revolutionises the prediction of the required number of k-barriers for rapid IDP. To develop and validate our approach, we synthetically generated pertinent features using Monte-Carlo simulations. These features encompass essential parameters such as the concerned region’s area, effective transmission range, effective sensing range, number of sensor nodes, and the fading parameter. Training the F-TLBO-ID algorithm with these features yielded exceptional results, accurately predicting the required number of k-barriers with an impressive correlation coefficient (R = 0.99), minimal Root Mean Square Error (RMSE = 11.32), and negligible bias (−3.66). To benchmark the performance of our F-TLBO-ID algorithm, we conducted comprehensive comparisons with fine-tuned benchmark algorithms, including AutoML, GPR, GRNN, RF, RNN, SVM, and ANN. Additionally, we evaluated the algorithm against 11 different variants of nature-inspired algorithms. Remarkably, our F-TLBO-ID algorithm outperformed all these methods in terms of accuracy, firmly establishing its superiority. Finally, we validated the performance of the F-TLBO-ID algorithm using publicly available datasets. The results were highly satisfactory, exhibiting a strong correlation coefficient (R = 0.84), acceptable RMSE (36.24), and minimal bias (−7.17). This study offers a robust and reliable algorithm to predict the required barriers for fast IDP, surpassing the accuracy of existing benchmark algorithms. By implementing our proposed algorithm, the efficiency of IDP systems at international borders can be significantly improved, ultimately enhancing security and facilitating smooth border operations.

Optimization of Air Handler Controllers for Comfort Level in Smart Buildings Using Nature Inspired Algorithm

This research seeks to improve the temperature control of AHU in building sub-levels using optimization algorithms. Specifically, the study applies the FA and PSO algorithms to optimize the PID control of AHU’s temperature. The study addresses the issue of temperature control in building sub-levels, which is a common challenge in HVAC systems. The study uses optimization algorithms and a nonlinear model to improve temperature control and reduce fluctuations in temperature from the desired setting. Additionally, a NL-ARX algorithm is utilized to create a nonlinear model based on the thermal dynamics and energy behavioral patterns of ACMV cooling systems. The study evaluates the performance of three controllers—PID, FA-PID, and PSO-PID—based on ITSE as a performance index. The study compares the performance of these controllers to achieve the desired temperature setting, and it analyses the influence of temperature regulation on occupant comfort levels. In this study, we compare different controllers using ITSE as a performance indicator. This shows how well different optimization algorithms work at setting the right temperature. The research gap is the lack of efficient temperature control solutions in building sub-levels that can optimize occupant comfort and energy efficiency. The experimental findings confirm that PSO-PID outperforms conventional PID and FA-PID optimization in terms of achieving the goal objective via computational complexity. Overall, this study’s focus is to explore and compare different optimization algorithms to improve temperature control and occupant comfort in building sub-levels.

A Hybrid Bald Eagle-Crow Search Algorithm for Gaussian mixture model optimisation in the speaker verification framework

A mutualism of nature-inspired algorithms, the Bald Eagle Search Algorithm (BESA) with the Crow Search Algorithm (CSA), is proposed for Gaussian Mixture Model optimisation in the speaker verification framework. BESA simulates bald eagles’ hunting behaviour, whereas CSA simulates crows’ food-hiding behaviour. BESA excels in exploitation but has limitations in exploration, whereas CSA showcases strong exploration capabilities but faces challenges in effective exploitation. BESA’s hunting characteristic combines crows’ memory update to generate a Hybrid Bald Eagle-Crow Search Algorithm (HBE-CSA). The ability of the proposed HBE-CSA is tested on clean speech utterances from the Librispeech dataset and eight real-life noisy conditions from the AURORA dataset at four different Signal-to-Noise Ratios (SNRs). The comparative analysis shows that the proposed HBE-CSA converges with higher log-likelihood than individual search algorithms and the state-of-the-art Expectation Maximisation algorithm. Detection error trade-off curves validate the results of convergence behaviour, indicating that best speaker models are built using the proposed HBE-CSA. The noise analysis shows the smallest equal error rate for the proposed HBE-CSA at different SNRs than competing algorithms. The proposed HBE-CSA holds promise for optimising models not only in speaker verification but also across other areas of speech processing.

A comparative evaluation of nature-inspired algorithms for feature selection problems

Feature selection is a critical component of machine learning and data mining which addresses challenges like irrelevance, noise, redundancy in large-scale data etc., which often result in the curse of dimensionality. This study employs a K-nearest neighbour wrapper to implement feature selection using six nature-inspired algorithms, derived from human behaviour and mammal-inspired techniques. Evaluated on six real-world datasets, the study aims to compare the performance of these algorithms in terms of accuracy, feature count, fitness, convergence and computational cost. The findings underscore the efficacy of the Human Learning Optimization, Poor and Rich Optimization and Grey Wolf Optimizer algorithms across multiple performance metrics. For instance, for mean fitness, Human Learning Optimization outperforms the others, followed by Poor and Rich Optimization and Harmony Search. The study suggests the potential of human-inspired algorithms, particularly Poor and Rich Optimization, in robust feature selection without compromising classification accuracy.

Formulation of a multi-period multi-echelon location-inventory-routing problem comparing different nature-inspired algorithms

Formulation of a multi-period multi-echelon location-inventory-routing problem comparing different nature-inspired algorithms

A Systematic Review on Various Task Scheduling Algorithms in Cloud Computing

Task scheduling in cloud computing involves allocating tasks to virtual machines based on factors such as node availability, processing power, memory, and network connectivity. In task scheduling, we have various scheduling algorithms that are nature-inspired, bio-inspired, and metaheuristic, but we still have latency issues because it is an NP-hard problem. This paper reviews the existing task scheduling algorithms modelled by metaheuristics, nature-inspired algorithms, and machine learning, which address various scheduling parameters like cost, response time, energy consumption, quality of services, execution time, resource utilization, makespan, and throughput, but do not address parameters like trust or fault tolerance. Trust and fault tolerance have an impact on task scheduling; trust is necessary for tasks and assigning responsibility to systems, while fault tolerance ensures that the system can continue to operate even when failures occur. A balance of trust and fault tolerance gives a quality of service and efficient task scheduling; therefore, this paper has analysed parameters like trust and fault tolerance and given research directions.

Hybridized artificial intelligence models with nature-inspired algorithms for river flow modeling: A comprehensive review, assessment, and possible future research directions

River flow (Qflow) is a hydrological process that considerably impacts the management and sustainability of water resources. The literature has shown great potential for nature-inspired optimized algorithms (NIOAs), like hybrid artificial intelligence (HAI) models, for Qflow modeling. Qflow forecasting needs to be accurate, robust, reliable, and capable of resolving complex non-linear problems to support the decision authority in local and national governments and NGOs. This extensive survey provides a literature review of 100-plus high-impact factor journal articles on developing NIOAs models during 2000–2022. This encompasses a comprehensive review of the established research in different climatic zones, NIOA types, artificial intelligence (AI) models, the input parameters used for model development, Qflow on different time scales, and model evaluation using a wide range of performance metrics. The review also assessed and evaluated several components of relevant literature, along with detailing the existing research gaps. Moreover, the global research gap with future direction is discussed based on current research limitations and possibilities. The data availability evaluation and futuristic suggestions are drafted logically. The review revealed the superiority of the NIOAs among all applied algorithms in the literature. Further, the review concludes that there is a need to improve technical aspects of Qflow forecasting and bridge the gap between scientific research, hydrometeorological model development, and real-world flood and drought management using probabilistic nature inspired (NI) forecasts, especially through effective communication.

Stability Analysis and Navigational Techniques of Wheeled Mobile Robot: A Review

Wheeled mobile robots (WMRs) have been a focus of research for several decades, particularly concerning navigation strategies in static and dynamic environments. This review article carefully examines the extensive academic efforts spanning several decades addressing navigational complexities in the context of WMR route analysis. Several approaches have been explored by various researchers, with a notable emphasis on the inclusion of stability and intelligent capabilities in WMR controllers attracting the attention of the academic community. This study traces historical and contemporary WMR research, including the establishment of kinetic stability and the construction of intelligent WMR controllers. WMRs have gained prominence in various applications, with precise navigation and efficient control forming the basic prerequisites for their effective performance. The review presents a comprehensive overview of stability analysis and navigation techniques tailored for WMRs. Initially, the exposition covers the basic principles of WMR dynamics and kinematics, explaining the different wheel types and their associated constraints. Subsequently, various stability analysis approaches, such as Lyapunov stability analysis and passivation-based control, are discussed in depth in the context of WMRs. Starting an exploration of navigation techniques, the review highlights important aspects including path planning and obstacle avoidance, localization and mapping, and trajectory tracking. These techniques are carefully examined in both indoor and outdoor settings, revealing their benefits and limitations. Finally, the review ends with a comprehensive discussion of the current challenges and possible routes in the field of WMR. The discourse includes the fusion of advanced sensors and state-of-the-art control algorithms, the cultivation of more robust and reliable navigation strategies, and the continued exploration of novel WMR applications. This article also looks at the progress of mobile robotics during the previous three decades. Motion planning and path analysis techniques that work with single and multiple mobile robots have been discussed extensively. One common theme in this research is the use of soft computing methods to give mobile robot controllers cognitive behaviors, such as artificial neural networks (ANNs), fuzzy logic control (FLC), and genetic algorithms (GAs). Nevertheless, there is still a dearth of applications for mobile robot navigation that leverage nature-inspired algorithms, such as firefly and ant colony algorithms. Remarkably, most studies have focused on kinematics analysis, with a small number also addressing dynamics analysis.

MOEA/D vs. NSGA-II: A Comprehensive Comparison for Multi/Many Objective Analog/RF Circuit Optimization through a Generic Benchmark

Thanks to the enhanced computational capacity of modern computers, even sophisticated analog/radio frequency (RF) circuit sizing problems can be solved via electronic design automation (EDA) tools. Recently, several analog/RF circuit optimization algorithms have been successfully applied to automatize the analog/RF circuit design process. Conventionally, metaheuristic algorithms are widely used in optimization process. Among various nature-inspired algorithms, evolutionary algorithms (EAs) have been more preferred due to their superiorities (robustness, efficiency, accuracy etc.) over the other algorithms. Furthermore, EAs have been diversified and several distinguished analog/RF circuit optimization approaches for single-, multi-, and many-objective problems have been reported in the literature. However, there are conflicting claims on the performance of these algorithms and no objective performance comparison has been revealed yet. In the previous work, only a few case study circuits have been under test to demonstrate the superiority of the utilized algorithm, so a limited comparison has been made for only these specific circuits. The underlying reason is that the literature lacks a generic benchmark for analog/RF circuit sizing problem. To address these issues, we propose a comprehensive comparison of the most popular two evolutionary computation algorithms, namely Non-Sorting Genetic Algorithm-II and Multi-Objective Evolutionary Algorithm based Decomposition, in this article. For that purpose, we introduce two ad hoc testbenches for analog and RF circuits including the common building blocks. The comparison has been made at both multi- and many-objective domains and the performances of algorithms have been quantitatively revealed through the well-known Pareto-optimal front quality metrics.

Trust And Energy-Aware Routing Protocol for Wireless Sensor Networks Based on Secure Routing

Wireless Sensor Network (WSN) is a network area that includes a large number of nodes and the ability of wireless transmission. WSNs are frequently employed for vital applications in which security and dependability are of utmost concern. The main objective of the proposed method is to design a WSN to maximize network longevity while minimizing power usage. In a WSN, trust management is employed to encourage node collaboration, which is crucial for achieving dependable transmission. In this research, a novel Trust and Energy Aware Routing Protocol (TEARP) in wireless sensors networks is proposed, which use blockchain technology to maintain the identity of the Sensor Nodes (SNs) and Aggregator Nodes (ANs). The proposed TEARP technique provides a thorough trust value for nodes based on their direct trust values and the filtering mechanisms generate the indirect trust values. Further, an enhanced threshold technique is employed to identify the most appropriate clustering heads based on dynamic changes in the extensive trust values and residual energy of the networks. Lastly, cluster heads should be routed in a secure manner using a Sand Cat Swarm Optimization Algorithm (SCSOA). The proposed method has been evaluated using specific parameters such as Network Lifetime, Residual Energy, Throughpu,t Packet Delivery Ratio, and Detection Accuracy respectively. The proposed TEARP method improves the network lifetime by 39.64%, 33.05%, and 27.16%, compared with Energy-efficient and Secure Routing (ESR), Multi-Objective nature-inspired algorithm based on Shuffled frog-leaping algorithm and Firefly Algorithm (MOSFA) , and Optimal Support Vector Machine (OSVM).

Energy-Efficient Train Operation Optimization in Istanbul Metro Network Considering Variable Passenger Numbers

This manuscript presents a speed trajectory optimization to minimize driving energy consumption using four different nature-inspired algorithms. For this purpose, a model was developed that can simulate motion of a single train. To test the train motion model and speed trajectories, Istanbul M3 subway line was modeled with its actual parameters such as gradient, curve, and speed limit. Concerning computational time and energy usage, a straightforward speed profile and proposed speed profiles by the algorithms have been compared. Afterwards, variable passenger numbers at each station in a certain hour were taken as dynamic mass input. With the marine predator algorithm (MPA) that finds the best result among the four, speed profile optimization was performed again under the effect of unstable mass. Finally, a train was operated by a driver according to the optimal speed profile. As a result, energy saving is 18.75% in simulation environment and 21.27% in real-life test.

3EDANFIS: Three Channel EEG-Based Depression Detection Technique with Hybrid Adaptive Neuro Fuzzy Inference System

Background: Depression is a mental disorder that often negatively impacts the actions and feelings of the affected person. No laboratory tests are available to detect and properly diagnose depression. Presently, the detection of depression is done based on standardized questionnaires like Diagnostic and Statistical Manual of Mental Disorders-fifth edition (DSM-V) and Hamilton Depression Rating Scale (HAM-D) which is subjective in nature. Objective: The purpose of the study is to propose a framework for more accurate detection of depression from EEG signals using only three channels, which makes the system portable as well as efficient. Methods: In this study, we propose a classification model using EEG signal with the help of Adaptive Neuro Fuzzy Inference System optimized by nature-inspired algorithm. The proposed model is efficient, accurate, and portable as the features are extracted from only three channels, namely, Fp1, Fp2, and Fz. The three Data Channel (3EDANFIS) Adaptive Neuro Fuzzy Inference System (ANFIS) for detection of depression as well as three variants of Hybrid ANFIS – Adaptive Neuro Fuzzy Inference System-Genetic Algorithm (ANFIS-GA), Adaptive Neuro Fuzzy Inference System- Particle Swam Optimization (ANFIS-PSO) and Adaptive Neuro Fuzzy Inference System- Firefly Algorithm (ANFIS-FA) has been analyzed in this study. The features extracted are delta, theta, alpha, and beta and their corresponding sub-bands delta1, delta2, theta1, theta2, alpha1, alpha2, beta1, and beta2. Genetic Algorithm (GA), Particle Swam Optimization (PSO), and Firefly Algorithm (FA) are all nature-inspired metaheuristic algorithms which are used to optimize ANFIS by adapting the premise and consequent parameters. Results: The analysis showed that the GA and FA perform equally well in optimizing ANFIS with the highest accuracy of 83.33 % using delta1 power as well as delta power. Overall accuracy of the ANFIS-GA is found to be higher than that of the ANFIS-PSO, ANFIS-FA, and ANFIS. It was also found that the sub-band classification accuracy is higher than that of the band itself for delta, theta, and alpha bands. In case of the ANFIS, ANFIS-GA, ANFIS-PSO, and ANFIS-FA, delta1 was found to be having higher accuracy than delta power, theta1 was found to be having higher accuracy than theta power, and both alpha1 and alpha2 showed higher accuracy than alpha power. Conclusion: The use of only three EEG channels for data recording makes our technique to be more feasible, portable, convenient, and faster and hence can act as an adjunct tool for psychiatrists in the future.

Does the Field of Nature-Inspired Computing Contribute to Achieving Lifelike Features?

The main idea behind artificial intelligence was simple: what if we study living systems to develop new, practical computing systems that possess "lifelike" properties? And that's exactly how evolutionary computing emerged. Researchers came up with ideas inspired by the principles of evolution to develop intelligent methods to tackle hard problems. The efficacy of these methods made researchers seek inspiration in living organisms and systems and extend the evolutionary concept to other nature-inspired ideas. In recent years, nature-inspired computing has exhibited an exponential increase in the number of algorithms that are presented each year. Authors claim that they are inspired by a behavior found in nature to come up with a lifelike algorithm. However, the mathematical background does not match the behavior in the majority of these cases. Thus the question is, do all nature-inspired algorithms remain lifelike? Also, are there any ideas included that contribute to computing? This study aims to (a) present some nature-inspired methods that contribute to achieving lifelike features of computing systems and (b) discuss if there is any need for new lifelike features.

Classification of risk factors of climate change on infectious diseases using Bio-Inspired Algorithms

A new index of investigative approaches is being developed to help identify emerging infections and to detect the increased risks factors of infectious disease (IDs) occurrences that are expected to occur with climate change. The impacts of Climate Change with direct effects on human health through climate extremes and indirectly through infectious diseases are enormous. Hence, it requires study to determine correlations between Climate Change and Infectious Diseases using clinically validated data to improve behavioural health plans and early warning of public health risks. This work employs Meteorological and Infectious Disease data from the Figshare Data Repository, NiMET, NIH, WHO and literature, processes these data using SciKit Learn Preprocessing Package in Python and characterizes the risk factors of Climate Change Features: Humidity, Minimum Temperature, Maximum Temperature and Rainfall on Infectious Disease: Malaria, Pneumonia and Diarrhea using Nature-Inspired Algorithm using Artificial Neural Network (ANN) and Random Forest (RF) Algorithm with the R Statistical Programming Language. The work adopts Design Science Research (DSR) Methodology to analyze and classifies the risk factors of climate change on infectious diseases as well as evaluates the performances of selected Nature-Inspired Algorithms in classifying selected climatic factors and associated impacts on infectious diseases. Results obtained demonstrated that the RF performed better than ANN Algorithms with 96.9% and 95% accuracies respectively. Both models indicated that Rainfall and Temperature variations were common risks factors that indicated highest weights impacting the emergence and incidence of Infectious Diseases in Nigeria.