Bio-inspired Search Research Articles

HPB3C-3PG algorithm: A new hybrid global optimization algorithm and its application to plant classification

This paper proposes a hybrid bio-inspired search and optimization algorithm that combines the strengths of the PB3C (Parallel Big Bang Big Crunch) and 3PGA (3 Parent Genetic Algorithm) algorithms. The hybrid algorithm employs a single population-based evolutionary search coupled with multi-population parallel processing techniques to address optimization problems. The proposed algorithm is implemented in MATLAB software. We evaluate the performance of the proposed algorithm on the CEC2021 standard test bench suite. The performance of the proposed approach is compared with that of the other nine algorithms. The comparative analysis shows that the proposed hybrid PB3C and 3PGA algorithms performed better than the other nine optimization algorithms. Furthermore, this chapter proposes an HPB3C-3PGA-based approach to evolve the near-optimal architecture of CNN. The proposed plant image classification approach is implemented in Python and compared with 12 other approaches. The proposed approach achieved an accuracy of 98.96% on the Mendeley dataset and 98.97% on the CVIP100 dataset. The proposed approach outperforms all other approaches for the plant leaf classification problem. This research significantly contributes to overcoming limitations in existing approaches, providing a robust solution for optimization problems and image classification tasks.

Optimizing construction project management via genetic algorithms – A state-of-the-art review

Construction projects involve complex trade-offs between conflicting objectives such as minimizing duration and costs while ensuring feasibility, safety, and quality. This paper reviews research on leveraging genetic algorithms (GAs)–adaptive bioinspired search techniques–to enhance decision-making in construction management spanning planning, scheduling, and control. The versatility of GAs in tackling combinatorial optimization problems is established through various applications optimizing workflows, resource allocation, and crew coordination for enhanced project performance. Empirical analyses validate GAs generating superior trade-off solutions regarding project timeline, budgetary compliance, profitability, and safety over conventional methods such as mathematical programming or regression. Challenges remain regarding further enhancing the solution quality and fine-tuning the GA parameters. Nonetheless, by efficiently exploring vast solution spaces, GAs enable the scientific translation of construction data into actionable insights for augmenting complex project decisions. Exciting innovation opportunities exist in synergistically hybridizing GAs with promising contemporaries, such as machine learning, simulation, and robotics. Collectively, the existing literature underscores GAs' immense and relatively untapped potential of GAs to develop next-generation AI-powered tools for optimizing construction productivity, cost efficiency, sustainability, and lean automation.

A Curve Approximation Approach Using Bio-inspired Polar Coordinate Bald Eagle Search Algorithm

In this paper, a curve approximation approach using bio-inspired polar coordinate bald eagle search algorithm (PBES) is proposed. PBES algorithm is inspired by the spiral mechanism of bald eagle during predation. By introducing polar coordinate, the spiral predation process of the bald eagle will become more intuitive, which is more conducive for the algorithm to polar coordinate optimization problems. The initialization stage of PBES algorithm is modified to make the distribution of initialized individuals more uniform and some parameters are introduced to strengthen the exploration and exploitation capabilities of algorithm. The performance of the PBES algorithm is tested in three aspects: polar coordinate transcendental equation, curve approximation and robotic manipulator. The experimental results show that the PBES algorithm is superior to the well-known metaheuristic algorithms as it is effectively applicable for curve approximation problem.

Bio-inspired Search Approach Cross-Domain Location Mapping for Smart Mobile Service System

The health care service sector is very critical in every country. Although governments have made significant efforts to improve health infrastructure and train more qualified health professionals, the majority of them cannot find jobs within public health facilities. Furthermore, patients queue at health care facilities for hours daily for basic health care services while job creation and reducing these long queues remains a challenge in most developing countries. By leveraging the ubiquitousness of mobile technology, patients can be assisted to request health care services, to be delivered at their locations by qualified healthcare professionals. Given the critical nature of health care services, it is imperative to deliver services on-time without delays. In this regard, finding an optimal path between the service originator and the location of health care professionals is important. In this study, location data was used to provide near-optimal location information by mapping location data between patient and health professional domains. Our mapping approach centers on the hunting behavior of the Kestrel bird and through this, an algorithm is proposed for cross-domain location mapping. The mathematical model that is proposed in this study's major contribution as well as the application of the Kestrel-based Search Algorithm (KSA) for location data generation to find the optimal distance from an initial location. The result is promising in terms of the optimal distance between two locations using the haversine and equirectangular approxima¬tion formulas. The KSA was juxtaposed with other renowned meta-heuristic algorithms such as the BAT, Wolf Search Algorithm with Minus Previous Step (WSA-MP), and Ant Colony Optimization (ACO). Results obtained indicate that the equirectangular approxima¬tion formula with the KSA has the minimal distance which in practice implies that nature-inspired algorithms can be adopted in location mapping where randomization is essential, especially in a mobile health application.

The Steelmaking Process Parameter Optimization with a Surrogate Model Based on Convolutional Neural Networks and the Firefly Algorithm

High-strength low-alloy steels (HSLAs) are widely used in the structural body components of many domestic motor vehicles owing to their better mechanical properties and greater resistance. The real production process of HSLA steelmaking can be regarded as a model that builds on the relationship between process parameters and product quality attributes. A surrogate modeling method is used, and the resulting production process model can be applied to predict the optimal manufacturing process parameters. We used different methods in this paper, including linear regression, random forests, support vector regression, multilayer perception, and a simplified VGG model to build such a surrogate model. We then applied three bio-inspired search algorithms, namely particle swarm optimization, the artificial bee colony algorithm, and the firefly algorithm, to search for the optimal controllable manufacturing process parameters. Through experiments on 9000 test samples used for building the surrogate model and 299 test samples for making the optimal process parameter selection, we found that the combination of a simplified VGG model and the firefly algorithm was the most successful at reaching a success rate of 100%—in other words, when the product quality attributes of all test samples satisfy the mechanical requirements of the end products.

Integrated bio-search approaches with multi-objective algorithms for optimization and classification problem

Optimal selection of features is very difficult and crucial to achieve, particularly for the task of classification. It is due to the traditional method of selecting features that function independently and generated the collection of irrelevant features, which therefore affects the quality of the accuracy of the classification. The goal of this paper is to leverage the potential of bio-inspired search algorithms, together with wrapper, in optimizing multi-objective algorithms, namely ENORA and NSGA-II to generate an optimal set of features. The main steps are to idealize the combination of ENORA and NSGA-II with suitable bio-search algorithms where multiple subset generation has been implemented. The next step is to validate the optimum feature set by conducting a subset evaluation. Eight (8) comparison datasets of various sizes have been deliberately selected to be checked. Results shown that the ideal combination of multi-objective algorithms, namely ENORA and NSGA-II, with the selected bio-inspired search algorithm is promising to achieve a better optimal solution (i.e. a best features with higher classification accuracy) for the selected datasets. This discovery implies that the ability of bio-inspired wrapper/filtered system algorithms will boost the efficiency of ENORA and NSGA-II for the task of selecting and classifying features.

МНОГОУРОВНЕВЫЙ ПОДХОД ДЛЯ РЕШЕНИЯ ЗАДАЧИ ТРЕХМЕРНОЙ УПАКОВКИ БОЛЬШОЙ РАЗМЕРНОСТИ

The article considers one of the important combinatorial optimization problems, the problemof 3D packing of different elements in a fixed volume. It belongs to the class of NP-complex and difficultoptimization problems. The paper presents and describes the formulation of the 3D packingproblem, introduces a combined objective function that takes into account all the restrictions. Due tothe complexity of this task, a multilevel approach is proposed. It is consisting in dividing the 3D packingproblem into 3 subtasks and solving each subtask in a strict order. Moreover, for each of thesubtasks a unique set of objects is defined that are not repeated in the remaining subtasks. To implementa multi-level approach, the authors developed a combined bio-inspired algorithm based onevolutionary and genetic search. This approach can significantly reduce the time to obtain the result,partially solve the problem of preliminary convergence of the algorithms and obtain sets of quasioptimalsolutions in polynomial time. A software package was developed and computer-based algorithmsfor automated 3D packaging based on a combined bio-inspired search were implemented.A computational experiment was conducted on test examples (benchmarks). The packaging qualityobtained on the basis of the developed combined bio-inspired algorithm is on average 5 % higherthan the packaging results obtained using known algorithms, and the solution time is less than 5 % to20 %, which indicates the effectiveness of the proposed approach. The series of tests and experimentscarried out made it possible to refine the theoretical estimates of the time complexity of the packagingalgorithms. In the best case the time complexity of the O (n2) algorithms; in the worst, O (n3).

Design and optimisation of bio-inspired robotic stochastic search strategy

An autonomous robot's search strategy is the set of rules that it employs while looking for targets in its environment. Biological systems (e.g., foraging animals) provide useful inspirations for designing optimal stochastic search algorithms for autonomous robots. Due to the complexity of interaction between the robot and its environment, optimisation must be performed in high-dimensional parameter space. We analyse the dependence of search efficiency on environmental parameters and robot characteristics using response surface methodology (RSM), a technique originally developed for experimental design. In this study, the efficiency of a strategy focuses on Levy walk search strategies on two-dimensional landscapes with clumped resource distributions. We show how RSM techniques can be used to identify optimal parameter values and to describe how sensitive is the efficiency to the changes in these values.

Design and optimisation of bio-inspired robotic stochastic search strategy

Design and optimisation of bio-inspired robotic stochastic search strategy

CPU–GPU Utilization Aware Energy-Efficient Scheduling Algorithm on Heterogeneous Computing Systems

Nowadays, heterogeneous computing systems have proven to be a good solution for processing computation intensive high-performance applications. The main challenges for such large-scale systems are energy consumption, computing node CPU-GPU utilization dynamic variability, and so on. In response to these challenges, this study first provides heterogeneous computing systems architecture and parallel application job model. Then, we build system computing node CPU-GPU utilization model and analyze job execution energy consumption. We also deduce the optimal CPU-GPU utilization and job deadline scheduling constraint. Third, we propose a systems CPU-GPU utilization aware and energy-efficient heuristic greedy strategy (UEJS) to solve this job scheduling problem. To improve the algorithm global optimization ability, we design a hybrid particle swarm optimization algorithm (H-PSO), which incorporates the heuristic greedy strategy into the bio-inspired search optimization technique. The rigorous experimental results clearly demonstrate that our proposed H-PSO outperforms heuristic greedy strategy, Max-EAMin, and Genetic Algorithm in terms of the average energy consumption of jobs and system job rejection rate. In particular, H-PSO is better than UEJS by 36.5%, Max-EAMin by 36.3%, and GA by 46.7% in term of the job average energy consumption for heterogeneous system with high workload.

Optimum design of seismic isolation systems using metaheuristic search methods

This study addresses the optimum design of seismic isolated structures via metaheuristic search methods. Three recently developed bio-inspired search methods, namely crow search (CSA), whale optimization (WOA) and grey wolf optimizer (GWO), were employed to develop efficient design optimization algorithms for parameter optimization of seismic isolated structures. The developed design optimization algorithms have been applied to optimize a shear frame model with a base isolation system, where the main objective was to obtain isolation system parameters resulting in minimum roof acceleration without exceeding the isolation system displacement limits. The linear isolator system parameters, namely the damping ratio and isolator period, were optimized based on the time history analyses. The optimization procedure was performed for various isolation displacement limits and damping ratio ranges. The results obtained for this isolated structure by the three design optimization algorithms were compared with previously published results in the literature in order to achieve a verification of the developed algorithms. One of the main outcomes of this study is that the high damping ratio does not always guarantee optimum isolation parameters, while an optimum damping ratio is crucial for achieving the minimum roof acceleration.

Location-Based Online Task Assignment and Path Planning for Mobile Crowdsensing

Mobile crowdsensing has been a promising and cost-effective sensing paradigm for the Internet of Things by exploiting the proliferation and prevalence of sensor-rich mobile devices held/worn by mobile users. In this paper, we study the task assignment and path planning problem in mobile crowdsensing, which aims to maximize total task quality under constraints of user travel distance budgets. We first formulate the problem mathematically when all task and user arrival information is known a priori and prove it to be NP-hard. Then, we focus on studying the scenarios where users and tasks arrive dynamically and accordingly design four online task assignment algorithms, including quality/progress-based algorithm (QPA), task-density-based algorithm (TDA), travel-distance-balance-based algorithm (DBA), and bio-inspired travel-distance-balance-based algorithm (B-DBA). All the four algorithms work online for task assignment upon arrival of a new user. The former three algorithms work in a greedy manner for assigning tasks, one task each time, where the QPA prefers the task leading to largest ratio of task quality increment to travel cost, the TDA tends to guide user to high-task-density areas, and the DBA further considers travel distance balance information. The last algorithm B-DBA integrates the travel-distance-balance-aware metric in the DBA and bio-inspired search for further improved task assignment performance. Complexities of the proposed algorithms are deduced. Simulation results validate the effectiveness of our algorithms; B-DBA has the best performance among the four algorithms in terms of task quality, and furthermore, it outperforms existing work in this area.

Rigorous Running Time Analysis of a Simple Immune-Based Multi-Objective Optimizer for Bi-Objective Pseudo-Boolean Functions

A simple immune-based multi-objective optimizer (IBMO) is proposed, and a rigorous running time analysis of IBMO on three proposed bi-objective pseudo-Boolean functions (Bi-Trap, Bi-Plateau and Bi-Jump) is presented. The running time of a global simple evolutionary multi-objective optimizer (GSEMO) using standard bit mutation operator with IBMO using somatic contiguous hypermutation (CHM) operator is compared with these three functions. The results show that the immune-based hypermutation can significantly beat standard bit mutation on some well-known multi-objective pseudo-Boolean functions. The proofs allow us to understand the relationship between the characteristics of the problems and the features of the algorithms more deeply. These analysis results also give us a good inspiration to analyze and design a bio-inspired search heuristics.

A tutorial on multiobjective optimization: fundamentals and evolutionary methods

In almost no other field of computer science, the idea of using bio-inspired search paradigms has been so useful as in solving multiobjective optimization problems. The idea of using a population of search agents that collectively approximate the Pareto front resonates well with processes in natural evolution, immune systems, and swarm intelligence. Methods such as NSGA-II, SPEA2, SMS-EMOA, MOPSO, and MOEA/D became standard solvers when it comes to solving multiobjective optimization problems. This tutorial will review some of the most important fundamentals in multiobjective optimization and then introduce representative algorithms, illustrate their working principles, and discuss their application scope. In addition, the tutorial will discuss statistical performance assessment. Finally, it highlights recent important trends and closely related research fields. The tutorial is intended for readers, who want to acquire basic knowledge on the mathematical foundations of multiobjective optimization and state-of-the-art methods in evolutionary multiobjective optimization. The aim is to provide a starting point for researching in this active area, and it should also help the advanced reader to identify open research topics.

OPTIMIZATION OF ATTRIBUTE SELECTION MODEL USING BIO-INSPIRED ALGORITHMS

Attribute selection which is also known as feature selection is an essential process that is relevant to predictive analysis. To date, various feature selection algorithms have been introduced, nevertheless they all work independently. Hence, reducing the consistency of the accuracy rate. The aim of this paper is to investigate the use of bio-inspired search algorithms in producing optimal attribute set. This is achieved in two stages; 1) create attribute selection models by combining search method and feature selection algorithms, and 2) determine an optimized attribute set by employing bio-inspired algorithms. Classification performance of the produced attribute set is analyzed based on accuracy and number of selected attributes. Experimental results conducted on six (6) public real datasets reveal that the feature selection model with the implementation of bio-inspired search algorithm consistently performs good classification (i.e higher accuracy with fewer numbers of attributes) on the selected data set. Such a finding indicates that bio-inspired algorithms can contribute in identifying the few most important features to be used in data mining model construction.

Особливості програмної реалізації біоінспірованого методу м’якого декодування лінійних блокових кодів

The important part of many telecommunication technologies are linear block codes. The use of soft decoding can increasing the effectiveness of these codes. The expediency of application bioinspired approach for soft decoding relatively long linear block codes is grounded. The general principles bioinspired approach to soft decoding of linear block codes is considered. The basic steps and flowchart of the bioinspired method of decoding linear block codes are presented. A distinctive feature of the proposed method of decoding linear block codes is the use of generalized bioinspired search to determine the most likely base information vector and corresponding bipolar codeword. It is noted that the choice of a particular procedure bioinspired search should be carried out according to the parameters of the code, channel characteristics and performance requirements decoding. The ways of software implementation bioinspired method of decoding linear block codes are determined. Bioinspired decoding algorithm for linear block codes and features its implementation are presented. The list of input and output data for this algorithm is presented. Principles of the subprocess of the most reliable basis generative matrix and subprocess bioinspired search basic information word (and the corresponding bipolar codeword) are presented.

A multi-robot search using LEGO mindstorms

Embedded software is concurrent, real-time dependent, typically networked, must meet strict resource and high quality requirements, and often runs on cheap hardware. Altogether, this makes the education of embedded software designers a difficult challenge. In this paper, we present an embedded software design project, where students have to develop a multi-robot search using Lego mindstorms. The main idea is to confront the students with all the spites that are typically present in embedded systems, while at the same time giving them an algorithmically non-trivial problem to solve. To this end, we let the students use a bio-inspired search algorithm (particle-swarm optimization) to detect survivors (led by cries for help) in an unknown disaster zone using a number of Lego Mindstorm robots. We have executed this project simultaneously at the University of Potsdam and TU Berlin and discuss results and evaluations. We think that this project is very well suited for the education of embedded software engineers.

The application of bioinspired methods for solving vehicle routing problems

The paper presents an integrated approach to solving vehicle routing problems. The classification of modern transport problems of various types is given. A modified approach to solving problems of constructing optimal transport routes based on the use of hybrid intelligent subsystems is proposed. A new approach has been developed that combines bioinspired search methods, various evolution models and fuzzy control subsystems. Considerable attention is paid to the coordination of various components of the hybrid system. The program implementation of the proposed model is implemented. Experimental studies of the proposed models and algorithms are carried out.

On Easiest Functions for Mutation Operators in Bio-Inspired Optimisation

Understanding which function classes are easy and which are hard for a given algorithm is a fundamental question for the analysis and design of bio-inspired search heuristics. A natural starting point is to consider the easiest and hardest functions for an algorithm. For the (1+1) EA using standard bit mutation (SBM) it is well known that OneMax is an easiest function with unique optimum while Trap is a hardest. In this paper we extend the analysis of easiest function classes to the contiguous somatic hypermutation (CHM) operator used in artificial immune systems. We define a function MinBlocks and prove that it is an easiest function for the (1+1) EA using CHM, presenting both a runtime and a fixed budget analysis. Since MinBlocks is, up to a factor of 2, a hardest function for standard bit mutations, we consider the effects of combining both operators into a hybrid algorithm. We rigorously prove that by combining the advantages of k operators, several hybrid algorithmic schemes have optimal asymptotic performance on the easiest functions for each individual operator. In particular, the hybrid algorithms using CHM and SBM have optimal asymptotic performance on both OneMax and MinBlocks. We then investigate easiest functions for hybrid schemes and show that an easiest function for a hybrid algorithm is not just a trivial weighted combination of the respective easiest functions for each operator.

An approximation method for the P-median problem: A bioinspired tabu search and variable neighborhood search partitioning approach

The bioinspired systems are presented as a set of models that are based on the behavior of some biological systems and how act. These models can be expressed in data mining and operations research where clustering is a recurrent technique used to solve the P-median and territorial design problems. At this point, we have solved the P-median problem with a partitioning approach with bioinspired aspects and Variable Neighborhood Search (VNS). In this work we have improved the basic VNS search strategy and we present a bioinspired partitioning algorithm with optimization by Tabu Search (TS). This clusteringpartitioning problem under a bioinspired connotation has been proposed after observing some characteristics in common between clustering and human behavior during conflict situations, where some characteristics have been modeled accordingly. Finally we present our progress from our VNS implementation to our TS proposal.