A Review on Nature-based Swarm Intelligence Optimization Techniques and its Current Research Directions
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paper_title: Bacterial Foraging Optimization Algorithm: Theoretical Foundations, Analysis, and Applications

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Bacterial foraging optimization algorithm (BFOA) has been widely accepted as a global optimization algorithm of current interest for distributed optimization and control. BFOA is inspired by the social foraging behavior of Escherichia coli. BFOA has already drawn the attention of researchers because of its efficiency in solving real-world optimization problems arising in several application domains. The underlying biology behind the foraging strategy of E.coli is emulated in an extraordinary manner and used as a simple optimization algorithm. This chapter starts with a lucid outline of the classical BFOA. It then analyses the dynamics of the simulated chemotaxis step in BFOA with the help of a simple mathematical model. Taking a cue from the analysis, it presents a new adaptive variant of BFOA, where the chemotactic step size is adjusted on the run according to the current fitness of a virtual bacterium. Nest, an analysis of the dynamics of reproduction operator in BFOA is also discussed. The chapter discusses the hybridization of BFOA with other optimization techniques and also provides an account of most of the significant applications of BFOA until date.
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paper_title: Frequency Dependent Adaptive Chemotaxis in Bacterial Foraging Optimization for ST Segment Elevation Myocardial Infarction Prediction

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Background/Objectives : This paper enlightens the use of the renowned Bacterial Foraging Optimization (BFOA) Algorithm to classify and identify the occurrence of disease in an exact person. Methods/Statistical analysis : The classical BFOA predominantly relies on the chemotactic steps of the bacteria. The extracted fine-tuned ST segment data from the ElectroCardioGraph (ECG) signal is presented to the algorithm to find its best position. The search domain and the chemotactic step count are increased centered on the number of ST data delivered. To promote the varied chemotactic stages an external frequency is driven into the process to prove the system with more sensitivity. When the bacterium is close to the optimal value it oscillates and ensue further to investigate the global optima. Findings : Two structures are laid to improve and determine the exact global optima; 1. External frequency and 2. Adaptive chemotactic step, which deliver a better convergence rate. The system confirms an accuracy of 97% with 92.41% of sensitivity and 95.21% of positive predictive accuracy. The best position is found with a minimum chemotactic step and reproduction stages. Applications/Improvements: The proposed method can be used as a clinical assistant to procure a precise decision the field of cardiology, with more chemotactic steps the system can be improved to produce high accurate results.
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paper_title: Hybrid bee colony optimization for examination timetabling problems

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Swarm intelligence is a branch of artificial intelligence that focuses on the actions of agents in self-organized systems. Researchers have proposed a bee colony optimization (BCO) algorithm as part of swarm intelligence. BCO is a meta-heuristic algorithm based on the foraging behavior of bees. This study presents a hybrid BCO algorithm for examination timetabling problems. Bees in the BCO algorithm perform two main actions: forward pass and backward pass. Each bee explores the search space in forward pass and then shares information with other bees in the hive in backward pass. This study found that a bee decides to be either a recruiter that searches for a food source or a follower that selects a recruiter bee to follow on the basis of roulette wheel selection. In forward pass, BCO is supported along with other local searches, including the Late Acceptance Hill Climbing and Simulated Annealing algorithms. We introduce three selection strategies (tournament, rank and disruptive selection strategies) for the follower bees to select a recruiter to maintain population diversity in backward pass. The disruptive selection strategy outperforms tournament and rank selections. We also introduce a self-adaptive mechanism to select a neighborhood structure to enhance the neighborhood search. The proposed algorithm is evaluated against the latest methodologies in the literature with respect to two standard examination timetabling problems, namely, uncapacitated and competition datasets. We demonstrate that the proposed algorithm produces one new best result on uncapacitated datasets and comparable results on competition datasets.
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paper_title: Tuning of COCOMO Model Parameters by using Bee Colony Optimization

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Constructive Cost Model (COCOMO) used parameters for software effort estimation, which were calculated in 1981 by regression analysis of 63 types of project data; therefore applying these parameters to current project development will not generate accurate results. The objective of current research is applying Bee Colony Optimization (BCO) metaheuristic approach to optimize the parameters of COCOMO model for improving software cost estimation. The Bee Colony Optimization (BCO) is a new branch of Swarm Intelligence and has been applied successfully to various engineering disciplines. BCO approach is a “bottom-up” approach to modeling where special kinds of artificial agents are created by analogy with bees. These artificial agents or bees are used to solve complex combinatorial optimization problems. The proposed model validation is carried out using Interactive Voice Response software project dataset of a company. The results generated by the proposed model are compared to those obtained by methods proposed in the literature using Walston-Felix, SEL, Bailey-Basil, COCOMO II and Halstead models. The BCO approach generates various partial solutions and best solution is selected based on Mean Magnitude of Relative Error. The results obtained show that the proposed BCO based model is able to improve the accuracy of cost estimation and also outperform other models.
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paper_title: Distribution system reconfiguration for loss minimization using modified bacterial foraging optimization algorithm

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Abstract In this paper the network reconfiguration problem is formulated as non-linear objective optimization problem. The proposed solution to this problem is framed as a non-linear combinatorial optimization problem and status of switches is non-differentiable which is solved using modified bacterial foraging algorithm, the algorithm is modified for better convergence. The modified bacterial foraging algorithm is described in a general context and then applied specifically to the network reconfiguration problem. This program was tested on 16 bus, 33 bus & 69 bus test systems. The test results proved that the efficiency of modified bacterial foraging algorithm is better than other existing methodology.
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paper_title: Firefly-inspired algorithm for discrete optimization problems: An application to manufacturing cell formation

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Abstract The canonical firefly algorithm is basically developed for continuous optimization problems. However, lots of practical problems are formulated as discrete optimization problems. The main purpose of this paper is to present the discrete firefly algorithm (DFA) to solve discrete optimization problems. In the DFA, we define a firefly's position in terms of changes of probabilities that will be in one state or the other. Then by using this metaheuristic algorithm, the manufacturing cell formation problem is solved. To illustrate the behavior of the proposed model and verify the performance of the algorithm, we introduce a number of numerical examples to illustrate the use of the foregoing algorithm. The performance evaluation shows the effectiveness of the DFA. The proposed metaheuristic algorithm should thus be useful to both researchers and practitioners.
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paper_title: Adaptive comprehensive learning bacterial foraging optimization and its application on vehicle routing problem with time windows

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Abstract This paper proposes a variant of the bacterial foraging optimization (BFO) algorithm with time-varying chemotaxis step length and comprehensive learning strategy which we call adaptive comprehensive learning bacterial foraging optimization (ALCBFO). An adaptive non-linearly decreasing modulation model is used to keep a well balance between the exploration and exploitation of the proposed algorithm. The comprehensive learning mechanism maintains the diversity of the bacterial population and thus alleviates the premature convergence. Compared with the classical GA, PSO, the original BFO and two improved BFO (BFO-LDC and BFO-NDC) algorithm, the proposed ACLBFO shows significantly better performance in solving multimodal problems. We also assess the performance of the ACLBFO method on vehicle routing problem with time windows (VRPTW). Compared with three other BFO algorithms, the proposed algorithm is superior and confirms its potential to solve vehicle routing problem with time windows (VRPTW).
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paper_title: A Honey Bee Swarm Optimization Algorithm for Minimizing the Total Costs of Resources in MRCPSP

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In this paper, we introduce a multimode resource-constrained project scheduling problem with finish-to-start precedence relations among project activities, considering renewable and nonrenewable resource costs. We assume that renewable resources are rented and are not available in all periods of time of the project. In other words, there is a mandated ready date as well as a due date for each renewable resource type so that no resource is used before its ready date. However, the resources are permitted to be used after their due dates by paying penalty costs. The objective is to minimize the total costs of both renewable and nonrenewable resource usage. This problem is called multi-mode resource-constrained project scheduling problem with minimization the net present value of total weighted resource tardiness penalty cost (MRCPSP-DCTWRTPC) where for each activity, both renewable and nonrenewable resource requirements depend on activity mode. For this problem, we present a meta-heuristic algorithm based on a Honey Bee Swarm Optimization (HBSO) approach together with a prioritization rule for activities and several improvement and local search methods. Experimental results reveal the effectiveness and efficiency of the proposed algorithm for the problem in question.
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paper_title: Power System Stabilizers and Control of Reactive Power Compensator in Multi-Machine by Particle Swarm Optimization Algorithm

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The purpose of this article is to increase the stability and synchronization of multi-machine power system through power system stabilizers 3 band (pss3b). This new stabilizer system indicates better performance by changes in operating conditions and on the other hand, by setting Flexible AC Transmission System device (FACTS) this system can play a key role in limiting fluctuations. All system variables including pss3b and svc are designed by PSO algorithm in the article. The designed stabilizer has been used in a grid of 11 bus bar with two areas and four machines, and the pss and pss3b performances on increase of low frequency oscillation damping have been compared by applying three-phase fault at different working conditions. Finally the comparison between responses obtained from simulations with the same reference system response can prove advantage of this stabilizer and the newly designed system over previous ones in damping vibrations and stability increase.
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paper_title: Using the gray wolf optimizer for solving optimal reactive power dispatch problem

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Gray wolf optimizer (GWO) is employed in solving the optimal reactive power dispatch (ORPD) problems.Three case studies have been utilized to show the effectiveness of GWO.GWO able to find minimum loss and voltage deviation solution than those determined by other techniques. This paper presents the use of a new meta-heuristic technique namely gray wolf optimizer (GWO) which is inspired from gray wolves' leadership and hunting behaviors to solve optimal reactive power dispatch (ORPD) problem. ORPD problem is a well-known nonlinear optimization problem in power system. GWO is utilized to find the best combination of control variables such as generator voltages, tap changing transformers' ratios as well as the amount of reactive compensation devices so that the loss and voltage deviation minimizations can be achieved. In this paper, two case studies of IEEE 30-bus system and IEEE 118-bus system are used to show the effectiveness of GWO technique compared to other techniques available in literature. The results of this research show that GWO is able to achieve less power loss and voltage deviation than those determined by other techniques.
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Title: A Review on Nature-based Swarm Intelligence Optimization Techniques and its Current Research Directions
Section 1: Introduction
Description 1: This section introduces the concept of optimization and discusses the classification of optimization problems. It also provides an overview of nature-based optimization techniques and outlines the structure of the paper.

Section 2: Basic Concepts of Optimization Technique
Description 2: This section explains the fundamentals of optimization techniques, including traditional and non-traditional methods, and provides examples of different optimization paradigms.

Section 3: Study on Nature based Optimization Techniques and its Applications
Description 3: This section gives an overview of swarm intelligence and its applications in solving optimization problems. It discusses various swarm intelligence algorithms inspired by natural behaviors.

Section 4: Bacterial Foraging Algorithm
Description 4: This section describes the bacterial foraging optimization algorithm, its inspiration, and how it works. It includes discussions on parameters and pseudo-code.

Section 5: Research Scenario
Description 5: This section reviews the applications of bacterial foraging optimization and other swarm intelligence techniques in various research domains, summarizing important case studies and results.

Section 6: Firefly Algorithm
Description 6: This section covers the firefly algorithm, its inspiration from natural fireflies, and its working mechanism. The steps and pseudo-code of the algorithm are included.

Section 7: Ant Colony Optimization
Description 7: This section discusses ant colony optimization, its inspiration from ant behaviors, and its method of finding optimal solutions. It includes pseudo-code and modifications over the years.

Section 8: Applications of ACO
Description 8: This section lists the various applications of ant colony optimization, particularly in solving NP-hard problems and engineering applications.

Section 9: Cuckoo Optimization
Description 9: This section describes the cuckoo optimization algorithm, inspired by the brood parasitism behavior of cuckoos. It includes an explanation of the algorithm's functioning.

Section 10: Applications of Cuckoo Search
Description 10: This section discusses the applications of cuckoo search in various optimization problems, highlighting significant research outcomes.

Section 11: Bee Colony Optimization (BCO)
Description 11: This section presents bee colony optimization, inspired by the foraging behavior of bees. It discusses the process and pseudo-code of the algorithm.

Section 12: Particle Swarm Optimization (PSO)
Description 12: This section explains particle swarm optimization, inspired by social behaviors of animals and insects. It discusses the mechanics, functioning, and application of the algorithm.

Section 13: Grey Wolf Optimization (GWO)
Description 13: This section describes grey wolf optimization, inspired by the social hierarchy and hunting mechanism of grey wolves. It includes explanations and applications of the algorithm.

Section 14: Conclusions
Description 14: This section summarizes the key points of the paper, comparing various nature-based optimization algorithms and suggesting the potential for hybrid algorithms to overcome individual limitations.

Section 15: References
Description 15: This section lists the references cited in the paper.