Population-based Algorithm Research Articles

Optimal design of second generation current conveyor using craziness-based particle swarm optimisation

In this paper, a population-based meta-heuristic search algorithm called craziness-based particle swarm optimisation (CRPSO) has been employed for the optimal design of positive second-generation current conveyor based on a trans-linear loop (CCII+). CRPSO accepts several random variables for encompassing improved and quicker search and utilisation in multidimensional search space. The main goal of this work is to optimally size the CCII+ building block to attain the suitable aspect ratios of the MOS transistors. To enhance the performance of a CCII+ design, it needs to increase the higher cut-off frequency of the current signal (fCI) as well as to decrease the input X-port parasitic resistance (RX). Accordingly, the optimisation problem is developed as a bi-objective problem of minimisation to obtain the least value of RX and the superior value of fCI. The current conveyor is simulated and validated using UMC 180 nm CMOS technology with a power supply of ±2.5 V in Cadence Virtuoso XL Design Environment.

Adaptation of Population Size in Differential Evolution and Its Effects on Localization of Target Nodes

The differential evolution (DE) is a well known population-based evolutionary algorithm that has shown capabilities for solving real-world problems such as resource allocation, multicast routing, and localization of target nodes. However, the accuracy of the DE, like other evolutionary algorithms, depends on the settings of its control parameters. The localization of target nodes is highly nonlinear and multi-modal, which may trap the DE in a local optimum. A local optimum may be avoided by a proper selection of the control parameters. One of the key control parameters is the population size (PS), which affects directly the localization accuracy and computational complexity. Finding an adequate PS throughout the evolution process is a challenging task. Even if an adequate PS is found it may not be the adequate PS anymore when the scenario of a problem changes. Although several approaches have been proposed for adapting the PS, they have not been evaluated when solving the localization problem. In this paper, a comprehensive comparison in terms of accuracy and computational demand is conducted among the state-of-the-art PS adaptation techniques when employed with the DE for solving the localization problem of target nodes in various scenarios.We also propose three new PS adaptation techniques, namely, exponential, parabolic, and logistic reduction. The results from extensive numerical simulations show that, after setting the initial PS properly, there is no technique that outperforms the others in practically all the scenario of the localization problem. Additionally, the DE with the proposed techniques provides competitive localization accuracy with considerably less computational complexity. Specifically, The proposed approaches reduce the computational demand by approximately 50% over the standard DE in all the scenarios considered here.

A Quantum-Inspired Sperm Motility Algorithm

<abstract> <p>Sperm Motility Algorithm (SMA), inspired by the human fertilization process, was proposed by Abdul-Raof and Hezam <sup>[<xref ref-type="bibr" rid="b1">1</xref>]</sup> to solve global optimization problems. Sperm flow obeys the Stokes equation or the Schrۤinger equation as its derived equivalent. This paper combines a classical SMA with quantum computation features to propose two novel Quantum-Inspired Evolutionary Algorithms: The first is called the Quantum Sperm Motility Algorithm (QSMA), and the second is called the Improved Quantum Sperm Motility Algorithm (IQSMA). The IQSMA is based on the characteristics of QSMA and uses an interpolation operator to generate a new solution vector in the search space. The two proposed algorithms are global convergence guaranteed population-based optimization algorithms, which outperform the original SMA in terms of their search-ability and have fewer parameters to control. The two proposed algorithms are tested using thirty-three standard dissimilarities benchmark functions. Performance and optimization results of the QSMA and IQSMA are compared with corresponding results obtained using the original SMA and those obtained from three state-of-the-art metaheuristics algorithms. The algorithms were tested on a series of numerical optimization problems. The results indicate that the two proposed algorithms significantly outperform the other presented algorithms.</p> </abstract>

Guest Editorial: Hybrid Approaches to Nature-Inspired Population-Based Intelligent Optimization for Industrial Applications

These days, hybrid nature-inspired population-based intelligent optimization methods are a wide range of the algorithms, which are used very often to solve real-world (industrial) optimizationproblems. As it was shown in [item 1) in the Appendix] by Slowik and Cpalka, nature-inspired methods can be divided into several groups of algorithms. In these groups of nature-inspired algorithms, we can find physics-based algorithms (gravitational search algorithm, harmony search algorithm, big bang-big crunch algorithm, etc.) and bio-inspired methods. In bio-inspired methods, we can find evolutionary algorithms (genetic algorithms, evolution strategies, genetic programming, etc.), swarm intelligence algorithms (particle swarm optimization algorithm, ant colony optimization algorithm, bat algorithm, etc.), immune algorithms (clonal selection algorithm, negative selection algorithm, etc.), and others (flower pollination algorithm, great Salomon run, Japanese tree frogs calling, etc.). These four selected groups of nature-inspired population-based optimization algorithm are commonly used in creating hybrid methods.

An Experimental Study of a Fuzzy Adaptive Emperor Penguin Optimizer for Global Optimization Problem

Emperor Penguin Optimizer (EPO) is a recently developed population-based meta-heuristic algorithm that simulates the huddling behavior of emperor penguins. Mixed results have been observed on the performance of EPO in solving general optimization problems. Within the EPO, two parameters need to be tuned (namely f and l) to ensure a good balance between exploration (i.e., roaming unknown locations) and exploitation (i.e., manipulating the current known best). Since the search contour varies depending on the optimization problem, the tuning of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">f</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">l</i> is problem-dependent, and there is no one-size-fits-all approach. To alleviate these problems, an adaptive mechanism can be introduced in EPO. This paper proposes a fuzzy adaptive variant of EPO, namely Fuzzy Adaptive Emperor Penguin Optimizer (FAEPO), to solve this problem. As the name suggests, FAEPO can adaptively tune the parameters <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">f</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">l</i> throughout the search based on three measures (i.e., quality, success rate, and diversity of the current search) via fuzzy decisions. A test suite of twelve optimization benchmark test functions and three global optimization problems (Team Formation Optimization-TFO, Low Autocorrelation Binary Sequence-LABS, and Modified Condition/Decision Coverage-MC/DC test case generation) were solved using the proposed algorithm. The respective solution results of the benchmark meta-heuristic algorithms were compared. The experimental results demonstrate that FAEPO significantly improved the performance of its predecessor (EPO) and gives superior performance against the competing meta-heuristic algorithms including an improved variant of EPO (IEPO).

AMBO: All Members-Based Optimizer for Solving Optimization Problems

There are many optimization problems in different branches of science that should be solved using an appropriate methodology. Population-based optimization algorithms are one of the most efficient approaches to solve this type of problems. In this paper, a new optimization algorithm called All Members-Based Optimizer (AMBO) is introduced to solve various optimization problems. The main idea in designing the proposed AMBO algorithm is to use more information from the population members of the algorithm instead of just a few specific members (such as best member and worst member) to update the population matrix. Therefore, in AMBO, any member of the population can play a role in updating the population matrix. The theory of AMBO is described and then mathematically modeled for implementation on optimization problems. The performance of the proposed algorithm is evaluated on a set of twenty-three standard objective functions, which belong to three different categories: unimodal, high-dimensional multimodal, and fixed-dimensional multimodal functions. In order to analyze and compare the optimization results for the mentioned objective functions obtained by AMBO, eight other well-known algorithms have been also implemented. The optimization results demonstrate the ability of AMBO to solve various optimization problems. Also, comparison and analysis of the results show that AMBO is superior and more competitive than the other mentioned algorithms in providing suitable solution.

An Improved Artificial Bee Colony Algorithm for Model-Free Active Noise Control: Algorithm and Implementation

Active noise control (ANC) usually adopts the filtered-x least mean square (FxLMS) algorithm. However, the FxLMS algorithm requires the identification of the secondary path. Problems of time-varying secondary path and imperfect secondary path modeling require model-free ANC algorithms for practical applications. In this paper, artificial bee colony (ABC) algorithm is improved to suitably develop a novel ANC algorithm without secondary path modeling. In addition, FxLMS algorithm may fall into local minima, while our proposed algorithm features its global optimization ability. In order to have anti-interference ability, in our algorithm, a forgetting factor is introduced into the fitness function. Moreover, the least mean square (LMS) algorithm is integrated into the ABC algorithm to enhance its exploitation ability, further accelerate the convergence rate and improve the noise reduction performance. Compared with some closely related population-based model free ANC algorithms, our proposed algorithm has anti-interference ability, faster convergence rate, and better noise reduction performance. Simulations and experiments are conducted to illustrate the effectiveness of the proposed algorithm.

Golden Search Optimization Algorithm

This study introduces an effective population-based optimization algorithm, namely the Golden Search Optimization Algorithm (GSO), for numerical function optimization. The new algorithm has a simple but effective strategy for solving complex problems. GSO starts with random possible solutions called objects, which interact with each other based on a simple mathematical model to reach the global optimum. To provide a fine balance between the explorative and exploitative behavior of a search, the proposed method utilizes a transfer operator in the adaptive step size adjustment scheme. The proposed algorithm is benchmarked with 23 unimodal, multimodal, and fixed dimensional functions and the results are verified by a comparative study with the well-known Gravitational Search Algorithm (GSA), Sine-Cosine Algorithm (SCA), Grey Wolf Optimization (GWO), and Tunicate Swarm Algorithm (TSA). In addition, the nonparametric Wilcoxon&#x2019;s rank sum test is performed to measure the pair-wise statistical performance of the GSO and provide a valid judgment about the performance of the algorithm. The simulation results demonstrate that GSO is superior and could generate better optimal solutions when compared with other competitive algorithms.

Dynamic production optimization based on transfer learning algorithms

Dynamic production optimization involves continuous cycle of model-predictive control initiated at specified times to maximize production net present value (NPV) throughout the expected life of the reservoir. Re-evaluating predictive models using traditional methods is computationally expensive. The existing methods for updating production controls, do not take the inter-cycle correlation into account. But such information is valuable for boosting the problem-solving efficiency on the current cycle by utilizing the experience or knowledge extracted from the previous developed cycles. Originally, transfer learning algorithms are difficult to be implemented into the oil and gas industry, due to its high computational cost and random or unavailable learning samples. Therefore, we propose a new transfer learning based optimization framework for dynamic production optimization problems. First, domain adaptation learning (DAL) is used to represent data between two inter-cycles, to decrease the dissimilarity between them. Second, extended boundary constraints (EBC) is a technique used to embed the optimization problem into the learning samples during DAL stage. EBC reduces the burden on computational facilities and makes the algorithm suitable for production optimization. Third, a transfer component analysis (TCA) method is used to simplify the data representation and also extract the data correlation. Then, the extracted correlation is used to produce an effective population for MOEAs. The developed framework is incorporated into three well-known evolutionary algorithms, nondominated sorting genetic algorithm II (NSGAII), multiobjective particle swarm optimization (MOPSO), and multiobjective evolutionary algorithm based on decomposition (MOEA/D) and one single objective optimizer, particle swarm optimization (PSO) for NPV maximization and robust optimization respectively. The proposed method is tested on a series of dynamic benchmark problems dynamic and a practical case based on a three channel reservoir model. Results showed that the proposed method reduces the number of simulation calls needed to reach optimum control options when using population-based evolutionary algorithms. Also, using the proposed technique, a higher NPV and better convergence speed in comparison to their original evolutionary algorithms is achieved.

An improved tangent search algorithm

The Tangent Search Algorithm (TSA) is a newly developed population-based meta-heuristic algorithm to solve complex optimization problems. It is based on the tangent function, which steers the given solution towards more promising regions of the search space. Though TSA has performed well for many optimization problems, the experimental analyses show that it suffers from the low exploration ability and slow convergence rate. This article proposes an improved TSA algorithm (iTSA). Using two concepts, ‘Fitness Weighted Search Strategy’ (FWSS) and ‘Opposition Based learning’ (OBL), iTSA is better in terms of exploration while maintaining the high convergence rate of TSA.•Fitness weighted search strategy (FWSS) is used to increase the exploration ability of TSA.•Opposition based learning (OBL) is used to increase the convergence speed of TSA.•Together, OBL and FWSS into iTSA outperformed the classical TSA and other considered state-of-the-art algorithms.The performance of the proposed iTSA is validated on two sets of test functions: CEC14 benchmark functions and a set of 21 well-known classical benchmark functions. The obtained results are compared with those obtained from the basic TSA and other considered state-of-the-art algorithms.

Philippine Eagle Optimization Algorithm

We propose the Philippine Eagle Optimization Algorithm (PEOA), which is a meta-heuristic and population-based search algorithm inspired by the territorial hunting behavior of the Philippine Eagle. From an initial random population of eagles in a given search space, the best eagle is selected and undergoes a local food search using the interior point method as its means of exploitation. The population is then divided into three subpopulations, and each subpopulation is assigned an operator which aids in the exploration. Once the respective operators are applied, the new eagles with improved function values replace the older ones. The best eagle of the population is then updated and conducts a local food search again. These steps are done iteratively, and the food searched by the final best eagle is the optimal solution of the search space. PEOA is tested on 20 optimization test functions with different modality, separability, and dimension properties. The performance of PEOA is compared to 13 other optimization algorithms. To further validate the effectiveness of PEOA, it is also applied to image reconstruction in electrical impedance tomography and parameter identification in a neutral delay differential equation model. Numerical results show that PEOA can obtain accurate solutions to various functions and problems. PEOA proves to be the most computationally inexpensive algorithm relative to the others examined, while also helping promote the critically endangered Philippine Eagle.

Ebola Optimization Search Algorithm: A New Nature-Inspired Metaheuristic Optimization Algorithm

Nature computing has evolved with exciting performance to solve complex real-world combinatorial optimization problems. These problems span across engineering, medical sciences, and sciences generally. The Ebola virus has a propagation strategy that allows individuals in a population to move among susceptible, infected, quarantined, hospitalized, recovered, and dead sub-population groups. Motivated by the effectiveness of this strategy of propagation of the disease, a new bio-inspired and population-based optimization algorithm is proposed. This study presents a novel metaheuristic algorithm named Ebola Optimization Search Algorithm (EOSA) based on the propagation mechanism of the Ebola virus disease. First, we designed an improved SIR model of the disease, namely SEIR-HVQD: Susceptible (S), Exposed (E), Infected (I), Recovered (R), Hospitalized (H), Vaccinated (V), Quarantine (Q), and Death or Dead (D). Secondly, we represented the new model using a mathematical model based on a system of first-order differential equations. A combination of the propagation and mathematical models was adapted for developing the new metaheuristic algorithm. To evaluate the performance and capability of the proposed method in comparison with other optimization methods, two sets of benchmark functions consisting of forty-seven (47) classical and thirty (30) constrained IEEE-CEC benchmark functions were investigated. The results indicate that the performance of the proposed algorithm is competitive with other state-of-the-art optimization methods based on scalability, convergence, and sensitivity analyses. Extensive simulation results show that the EOSA outperforms popular metaheuristic algorithms such as the Particle Swarm Optimization Algorithm (PSO), Genetic Algorithm (GA), and Artificial Bee Colony Algorithm (ABC). Also, the algorithm was applied to address the complex problem of selecting the best combination of convolutional neural network (CNN) hyperparameters in the image classification of digital mammography. Results obtained showed the optimized CNN architecture successfully detected breast cancer from digital images at an accuracy of 96.0%. The source code of EOSA is publicly available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/NathanielOy/EOSA_Metaheuristic</uri> .

SPaFE: A Crowdsourcing and Multimodal Recommender System to Ensure Travel Safety in a City

Existing navigation applications such as Google Maps, Apple maps provide a core service to users to find the shortest path or the path that takes the least amount of time towards a user&#x2019;s destination. The applications and other research efforts have been sought to include other features such as 3D maps, neighboring facilities, traffic information, and multi-modal alternate route suggestion based on user constraints. None of these, however, take into account an important factor: "user safety while commuting". Although the common perception of street crime is that it is primarily a problem in the third-world countries, current popular hashtags or topics (e.g. &#x201C;blacklifematters&#x201D;) indicate that it is now prevalent in other parts of the world as well. Even existing multi-modal alternative route recommender systems are incapable of adapting to a dynamic set of safety features, are unable to provide new safe path updates in response to real-time commuter responses, and take little or no account of historical on-road events when designing the safe algorithm. In light of the above background, we begin by developing a generic framework, namely On-road Risk Minimization Problem (ORMP).We then introduce a dynamic population-based algorithm, that we call Safe Path for Everyone (SPaFE), that solves ORMP using multi-modal historical and crowdsourced data. Finally, our extensive empirical results demonstrate that SPaFE markedly outperforms the state-of-the-art.

Sine (B): A single randomized Population-based algorithm for solving optimization problems

Heuristics are widely used in solving both constrained and unconstrained optimization problems. The population-based “JAYA” and “Rao-1” algorithms proposed in 2016 and 2020 respectively are popular among researchers due to their simplicity and easy adaptability. The absence of any tuning parameter makes them popular and is used effectively for solving real-world engineering problems also. This paper analyses these algorithms and proposes new algorithms, Sine (B) and Cosine (B). The performances of the algorithms are compared using thirty unconstrained benchmark functions available in the literature. Wilcoxon signed-rank test and Friedman test are conducted for statistical comparison. The results demonstrate that the new algorithms perform well and are comparable with JAYA and Rao-1 in terms of solution quality and speed. They can be suitably modified to solve constrained optimization problems also.

Optimal Reconfiguration of Power Distribution Network Using Hybrid Firefly and Particle Swarm Optimization Algorithm

Electrical energy has become the most essential requirement for working of today’s modern world. Power distribution networks (PDN) are required for providing power from substations to consumers but are subjected to power loss and voltage drop problems. These problems greatly affect the operational cost and voltage stability level of a PDN. Network reconfiguration (NR) is a cost effective approach to optimize PDN for reduction of power loss and improvement of voltage profile (VP). This paper presents an effective meta-heuristic, population-based algorithm for finding optimal configuration of a PDN. In particular Hybrid Firefly and Particle Swarm Optimization (HFPSO) algorithm is used. The HFPSO algorithm has enhanced exploration and exploitation strategies, and fast convergence rate. MATLAB software is used to implement the algorithm and IEEE 33-bus radial distribution system (RDS) is considered for NR. The results obtained show that active power loss is reduced by 46.35% from original value and minimum voltage is improved to 0.9572p.u. The comparison of obtained results with literature show that HFPSO algorithm has efficiently reduced active power loss and improved VP of the network.

Decision-making model based multilayer perceptrons for estimation of optimum design properties for truss structure

Many branches of the structural engineering discipline have many problems, which require the generating an optimum model for beam-column junction area reinforcement, weight lightening for members such a beam, column, slab, footing formed as reinforced concrete, steel, composite, and so on, cost arrangement for any construction, etc. With this direction, in the current study, a structural model as a 5-bar truss is handled to provide an optimum design by determining the fittest areas of bar sections. It is aimed that the total bar length is minimized through population-based metaheuristic algorithm as teaching-learning-based optimization (TLBO). Following, the decision-making model is developed via multilayer perceptrons (MLPs) by performing an estimation application to enable directly foreseen of the optimal section areas and total length of bars, besides, the approximation and correlation success are evaluated via some metrics. Thus, determination of the real optimal results of unknown and not-tested designs can be realized with this model in a short and effective time.

Performance Comparison of Recent Population-Based Metaheuristic Optimisation Algorithms in Mechanical Design Problems of Machinery Components

The optimisation of complex engineering design problems is highly challenging due to the consideration of various design variables. To obtain acceptable near-optimal solutions within reasonable computation time, metaheuristics can be employed for such problems. However, a plethora of novel metaheuristic algorithms are developed and constantly improved and hence it is important to evaluate the applicability of the novel optimisation strategies and compare their performance using real-world engineering design problems. Therefore, in this paper, eight recent population-based metaheuristic optimisation algorithms—African Vultures Optimisation Algorithm (AVOA), Crystal Structure Algorithm (CryStAl), Human-Behaviour Based Optimisation (HBBO), Gradient-Based Optimiser (GBO), Gorilla Troops Optimiser (GTO), Runge–Kutta optimiser (RUN), Social Network Search (SNS) and Sparrow Search Algorithm (SSA)—are applied to five different mechanical component design problems and their performance on such problems are compared. The results show that the SNS algorithm is consistent, robust and provides better quality solutions at a relatively fast computation time for the considered design problems. GTO and GBO also show comparable performance across the considered problems and AVOA is the most efficient in terms of computation time.

Evaluation of several initialization methods on arithmetic optimization algorithm performance

Abstract Arithmetic optimization algorithm (AOA) is one of the recently proposed population-based metaheuristic algorithms. The algorithmic design concept of the AOA is based on the distributive behavior of arithmetic operators, namely, multiplication (M), division (D), subtraction (S), and addition (A). Being a new metaheuristic algorithm, the need for a performance evaluation of AOA is significant to the global optimization research community and specifically to nature-inspired metaheuristic enthusiasts. This article aims to evaluate the influence of the algorithm control parameters, namely, population size and the number of iterations, on the performance of the newly proposed AOA. In addition, we also investigated and validated the influence of different initialization schemes available in the literature on the performance of the AOA. Experiments were conducted using different initialization scenarios and the first is where the population size is large and the number of iterations is low. The second scenario is when the number of iterations is high, and the population size is small. Finally, when the population size and the number of iterations are similar. The numerical results from the conducted experiments showed that AOA is sensitive to the population size and requires a large population size for optimal performance. Afterward, we initialized AOA with six initialization schemes, and their performances were tested on the classical functions and the functions defined in the CEC 2020 suite. The results were presented, and their implications were discussed. Our results showed that the performance of AOA could be influenced when the solution is initialized with schemes other than default random numbers. The Beta distribution outperformed the random number distribution in all cases for both the classical and CEC 2020 functions. The performance of uniform distribution, Rayleigh distribution, Latin hypercube sampling, and Sobol low discrepancy sequence are relatively competitive with the Random number. On the basis of our experiments’ results, we recommend that a solution size of 6,000, the number of iterations of 100, and initializing the solutions with Beta distribution will lead to AOA performing optimally for scenarios considered in our experiments.

A diversity metric for population-based metaheuristic algorithms

In Metaheuristic Algorithms (MA), the balance between exploration and exploitation is a common issue considered an open research problem in the MA community. Besides its particular parameters, another way to control the Exploration–Exploitation Balance in MA is by using a diversity metric (DM) as a guide. However, this procedure has two drawbacks: its computational cost and its effectiveness to represent the actual diversity of the population. This paper proposes a DM for real-coded candidate solutions. The approach utilizes surrogate hypervolumes to calculate the spatial distribution of the individuals in the population. In a comparison against five DM reported in the literature, our proposal achieves comparable results in terms of stability, sensitivity, and robustness in the presence of outliers, without significant increases in the computational cost.

Population initialization techniques for evolutionary algorithms for single-objective constrained optimization problems: Deterministic vs. stochastic techniques

The population initialization step is a common step in the majority (or even all) of evolutionary algorithms (EAs). There are many population initialization techniques. Due to the limited population size and the high dimensionality of many problems, there is little chance to cover the promising regions in the search space. From different perspectives, this paper compares the stochastic and deterministic population initialization techniques through comparing five of the well-known population initializers: Random number generator (RNG), Latin Hypercube, Sobol, Halton, and Kronecker. Due to the presence of many constraints in real-world applications, in this paper, we are focusing only on single-objective constrained optimization problems. Specifically, the goal is to investigate if there is a significant difference between these population initialization methods. In this paper, we explain theoretically and mathematically these different population initialization techniques. Moreover, different illustrative examples and visualizations are introduced to explain the behavior of each technique and compare different techniques from different perspectives. The results show that due to the high uniformity of the low-discrepancy sequences such as the Halton and Sobol sequences, the generated points using these sequences are more evenly distributed over the space than RNG, which is the commonly used technique for initializing the populations in EAs. Practically, using a set of benchmark functions, we investigate the use of each population initialization technique for initializing different population-based evolutionary algorithms. The results of our experiments prove that with sufficient numbers of iterations, the EAs are not sensitive to the initialization methods and there are no significant differences between the mentioned population initialization methods. Further, the low discrepancy methods enhance the exploration ability of EAs in early iterations.