ABC-X: a generalized,automatically configurable artificial bee colony framework

The artificial bee colony (ABC) algorithm is a popular metaheuristic that was originally conceived for tackling continuous function optimization tasks. Over the last decade, a large number of variants of ABC have been proposed, making it by now a well-studied swarm intelligence algorithm. Typically, in a paper on algorithmic variants of ABC algorithms, one or at most two of its algorithmic components are modified. Possible changes include variations on the search equations, the selection of candidate solutions to be explored, or the adoption of features from other algorithmic techniques. In this article, we propose to follow a different direction and to build a generalized ABC algorithm, which we call ABC-X. ABC-X collects algorithmic components available from known ABC algorithms into a common algorithm framework that allows not only to instantiate known ABC variants but, more importantly, also many ABC algorithm variants that have never been explored before in the literature. Automatic algorithm configuration techniques can generate from this template new ABC variants that perform better than known ABC algorithms, even when their numerical parameters are fine-tuned using the same automatic configuration process.     

Continuous optimization algorithms for tuning real and integer parameters of swarm intelligence algorithms

The performance of optimization algorithms, including those based on swarm intelligence, depends on the values assigned to their parameters. To obtain high performance, these parameters must be fine-tuned. Since many parameters can take real values or integer values from a large domain, it is often possible to treat the tuning problem as a continuous optimization problem. In this article, we study the performance of a number of prominent continuous optimization algorithms for parameter tuning using various case studies from the swarm intelligence literature. The continuous optimization algorithms that we study are enhanced to handle the stochastic nature of the tuning problem. In particular, we introduce a new post-selection mechanism that uses F-Race in the final phase of the tuning process to select the best among elite parameter configurations. We also examine the parameter space of the swarm intelligence algorithms that we consider in our study, and we show that by fine-tuning their parameters one can obtain substantial improvements over default configurations.     

A hybrid swarm-based algorithm for single-objective optimization problems involving high-cost analyses

In many technical fields, single-objective optimization procedures in continuous domains involve expensive numerical simulations. In this context, an improvement of the Artificial Bee Colony (ABC) algorithm, called the Artificial super-Bee enhanced Colony (AsBeC), is presented. AsBeC is designed to provide fast convergence speed, high solution accuracy and robust performance over a wide range of problems. It implements enhancements of the ABC structure and hybridizations with interpolation strategies. The latter are inspired by the quadratic trust region approach for local investigation and by an efficient global optimizer for separable problems. Each modification and their combined effects are studied with appropriate metrics on a numerical benchmark, which is also used for comparing AsBeC with some effective ABC variants and other derivative-free algorithms. In addition, the presented algorithm is validated on two recent benchmarks adopted for competitions in international conferences. Results show remarkable competitiveness and robustness for AsBeC.     

Searching for structural bias in particle swarm optimization and differential evolution algorithms

During the last two decades, a large number of metaheuristics have been proposed, leading to various studies that call for a deeper insight into the behaviour, efficiency and effectiveness of such methods. Among numerous concerns that are briefly reviewed in this paper, the presence of a structural bias (i.e. the tendency, not justified by the fitness landscape, to visit some regions of the search space more frequently than other regions) has recently been detected in simple versions of the genetic algorithm and particle swarm optimization. As of today, it remains unclear how frequently such a behaviour occurs in population-based swarm intelligence and evolutionary computation methods, and to what extent structural bias affects their performance. The present study focuses on the search for structural bias in various variants of particle swarm optimization and differential evolution algorithms, as well as in the traditional direct search methods proposed by Nelder–Mead and Rosenbrock half a century ago. We found that these historical direct search methods are structurally unbiased. However, most tested new metaheuristics are structurally biased, and at least some presence of structural bias can be observed in almost all their variants. The presence of structural bias seems to be stronger in particle swarm optimization algorithms than in differential evolution algorithms. The relationships between the strength of the structural bias and the dimensionality of the search space, the number of allowed function calls and the population size are complex and hard to generalize. For 14 algorithms tested on the CEC2011 real-world problems and the CEC2014 artificial benchmarks, no clear relationship between the strength of the structural bias and the performance of the algorithm was found. However, at least for artificial benchmarks, such old and structurally unbiased methods like Nelder–Mead algorithm performed relatively well. This is a warning that the presence of structural bias in novel metaheuristics may hamper their search abilities.     

A new multi-population artificial bee algorithm based on global and local optima for numerical optimization

Artificial Bee Colony (ABC) algorithm is a nature-inspired algorithm that showed its efficiency for optimizations. However, the ABC algorithm showed some imbalances between exploration and exploitation. In order to improve the exploitation and enhance the convergence speed, a multi-population ABC algorithm based on global and local optimum (namely MPGABC) is proposed in this paper. First, in MPGABC, the initial population is generated using both chaotic systems and opposition-based learning methods. The colony in MPGABC is divided into several sub-populations to increase diversity. Moreover, the solution search mechanism is modified by introducing global and local optima in the solution search equations of both employed and onlookers. The scout bees in the proposed algorithm are generated similarly to the initial population. Finally, the proposed algorithm is compared with several state-of-art ABC algorithm variants on a set of 13 classical benchmark functions. The experimental results show that MPGABC competes and outperforms other ABC algorithm variants.

     

Characterising the searchability of continuous optimisation problems for PSO

The focus of research in swarm intelligence has been largely on the algorithmic side with relatively little attention being paid to the study of problems and the behaviour of algorithms in relation to problems. When a new algorithm or variation on an existing algorithm is proposed in the literature, there is seldom any discussion or analysis of algorithm weaknesses and on what kinds of problems the algorithm is expected to fail. Fitness landscape analysis is an approach that can be used to analyse optimisation problems. By characterising problems in terms of fitness landscape features, the link between problem types and algorithm performance can be studied. This article investigates a number of measures for analysing the ability of a search process to improve fitness on a particular problem (called evolvability in literature but referred to as searchability in this study to broaden the scope to non-evolutionary-based search techniques). A number of existing fitness landscape analysis techniques originally proposed for discrete problems are adapted to work in continuous search spaces. For a range of benchmark problems, the proposed searchability measures are viewed alongside performance measures for a traditional global best particle swarm optimisation (PSO) algorithm. Empirical results show that no single measure can be used as a predictor of PSO performance, but that multiple measures of different fitness landscape features can be used together to predict PSO failure.     

A novel numerical optimization algorithm inspired from weed colonization

This paper introduces a novel numerical stochastic optimization algorithm inspired from colonizing weeds. Weeds are plants whose vigorous, invasive habits of growth pose a serious threat to desirable, cultivated plants making them a threat for agriculture. Weeds have shown to be very robust and adaptive to change in environment. Thus, capturing their properties would lead to a powerful optimization algorithm. It is tried to mimic robustness, adaptation and randomness of colonizing weeds in a simple but effective optimizing algorithm designated as Invasive Weed Optimization (IWO). The feasibility, the efficiency and the effectiveness of IWO are tested in details through a set of benchmark multi-dimensional functions, of which global and local minima are known. The reported results are compared with other recent evolutionary-based algorithms: genetic algorithms, memetic algorithms, particle swarm optimization, and shuffled frog leaping. The results are also compared with different versions of simulated annealing — a generic probabilistic meta-algorithm for the global optimization problem — which are simplex simulated annealing, and direct search simulated annealing. Additionally, IWO is employed for finding a solution for an engineering problem, which is optimization and tuning of a robust controller. The experimental results suggest that results from IWO are better than results from other methods. In conclusion, the performance of IWO has a reasonable performance for all the test functions.     

Set-based particle swarm optimization applied to the multidimensional knapsack problem

Particle swarm optimization algorithms have been successfully applied to discrete/valued optimization problems. However, in many cases the algorithms have been tailored specifically for the problem at hand. This paper proposes a generic set-based particle swarm optimization algorithm for use in discrete-valued optimization problems that can be formulated as set-based problems. A detailed sensitivity analysis of the parameters of the algorithm is conducted. The performance of the proposed algorithm is then compared against three other discrete particle swarm optimization algorithms from literature using the multidimensional knapsack problem and is shown to statistically outperform the existing algorithms.     

Small and Dim Target Detection via Lateral Inhibition Filtering and Artificial Bee Colony Based Selective Visual Attention

This paper proposed a novel bionic selective visual attention mechanism to quickly select regions that contain salient objects to reduce calculations. Firstly, lateral inhibition filtering, inspired by the limulus’ ommateum, is applied to filter low-frequency noises. After the filtering operation, we use Artificial Bee Colony (ABC) algorithm based selective visual attention mechanism to obtain the interested object to carry through the following recognition operation. In order to eliminate the camera motion influence, this paper adopted ABC algorithm, a new optimization method inspired by swarm intelligence, to calculate the motion salience map to integrate with conventional visual attention. To prove the feasibility and effectiveness of our method, several experiments were conducted. First the filtering results of lateral inhibition filter were shown to illustrate its noise reducing effect, then we applied the ABC algorithm to obtain the motion features of the image sequence. The ABC algorithm is proved to be more robust and effective through the comparison between ABC algorithm and popular Particle Swarm Optimization (PSO) algorithm. Except for the above results, we also compared the classic visual attention mechanism and our ABC algorithm based visual attention mechanism, and the experimental results of which further verified the effectiveness of our method.     

A Hybrid Search Algorithm for Swarm Robots Searching in an Unknown Environment

This paper proposes a novel method to improve the efficiency of a swarm of robots searching in an unknown environment. The approach focuses on the process of feeding and individual coordination characteristics inspired by the foraging behavior in nature. A predatory strategy was used for searching; hence, this hybrid approach integrated a random search technique with a dynamic particle swarm optimization (DPSO) search algorithm. If a search robot could not find any target information, it used a random search algorithm for a global search. If the robot found any target information in a region, the DPSO search algorithm was used for a local search. This particle swarm optimization search algorithm is dynamic as all the parameters in the algorithm are refreshed synchronously through a communication mechanism until the robots find the target position, after which, the robots fall back to a random searching mode. Thus, in this searching strategy, the robots alternated between two searching algorithms until the whole area was covered. During the searching process, the robots used a local communication mechanism to share map information and DPSO parameters to reduce the communication burden and overcome hardware limitations. If the search area is very large, search efficiency may be greatly reduced if only one robot searches an entire region given the limited resources available and time constraints. In this research we divided the entire search area into several subregions, selected a target utility function to determine which subregion should be initially searched and thereby reduced the residence time of the target to improve search efficiency.     

Glowworm swarm optimization for simultaneous capture of multiple local optima of multimodal functions

This paper presents glowworm swarm optimization (GSO), a novel algorithm for the simultaneous computation of multiple optima of multimodal functions. The algorithm shares a few features with some better known swarm intelligence based optimization algorithms, such as ant colony optimization and particle swarm optimization, but with several significant differences. The agents in GSO are thought of as glowworms that carry a luminescence quantity called luciferin along with them. The glowworms encode the fitness of their current locations, evaluated using the objective function, into a luciferin value that they broadcast to their neighbors. The glowworm identifies its neighbors and computes its movements by exploiting an adaptive neighborhood, which is bounded above by its sensor range. Each glowworm selects, using a probabilistic mechanism, a neighbor that has a luciferin value higher than its own and moves toward it. These movements—based only on local information and selective neighbor interactions—enable the swarm of glowworms to partition into disjoint subgroups that converge on multiple optima of a given multimodal function. We provide some theoretical results related to the luciferin update mechanism in order to prove the bounded nature and convergence of luciferin levels of the glowworms. Experimental results demonstrate the efficacy of the proposed glowworm based algorithm in capturing multiple optima of a series of standard multimodal test functions and more complex ones, such as stair-case and multiple-plateau functions. We also report the results of tests in higher dimensional spaces with a large number of peaks. We address the parameter selection problem by conducting experiments to show that only two parameters need to be selected by the user. Finally, we provide some comparisons of GSO with PSO and an experimental comparison with Niche-PSO, a PSO variant that is designed for the simultaneous computation of multiple optima. This work is partially supported by a project grant from the Ministry of Human Resources Development, India and by DRDO-IISc Mathematical Engineering Program.     

A competitive clustering particle swarm optimizer for dynamic optimization problems

Optimization in dynamic optimization problems (DOPs) requires the optimization algorithms not only to locate, but also to continuously track the moving optima. Particle swarm optimization (PSO) is a population-based optimization algorithm, originally developed for static problems. Recently, several researchers have proposed variants of PSO for optimization in DOPs. This paper presents a novel multi-swarm PSO algorithm, namely competitive clustering PSO (CCPSO), designed specially for DOPs. Employing a multi-stage clustering procedure, CCPSO splits the particles of the main swarm over a number of sub-swarms based on the particles positions and on their objective function values. The algorithm automatically adjusts the number of sub-swarms and the corresponding region of each sub-swarm. In addition to the sub-swarms, there is also a group of free particles that explore the environment to locate new emerging optima or exploit the current optima which are not followed by any sub-swarm. The adaptive search strategy adopted by the sub-swarms improves both the exploitation and tracking characteristics of CCPSO. A set of experiments is conducted to study the behavior of the proposed algorithm in different DOPs and to provide guidelines for setting the algorithm’s parameters in different problems. The results of CCPSO on a variety of moving peaks benchmark (MPB) functions are compared with those of several state-of-the-art PSO algorithms, indicating the efficiency of the proposed model.     

Particle Swarm Optimization with Scale-Free Interactions

The particle swarm optimization (PSO) algorithm, in which individuals collaborate with their interacted neighbors like bird flocking to search for the optima, has been successfully applied in a wide range of fields pertaining to searching and convergence. Here we employ the scale-free network to represent the inter-individual interactions in the population, named SF-PSO. In contrast to the traditional PSO with fully-connected topology or regular topology, the scale-free topology used in SF-PSO incorporates the diversity of individuals in searching and information dissemination ability, leading to a quite different optimization process. Systematic results with respect to several standard test functions demonstrate that SF-PSO gives rise to a better balance between the convergence speed and the optimum quality, accounting for its much better performance than that of the traditional PSO algorithms. We further explore the dynamical searching process microscopically, finding that the cooperation of hub nodes and non-hub nodes play a crucial role in optimizing the convergence process. Our work may have implications in computational intelligence and complex networks.     

Enhanced hybrid search algorithm for protein structure prediction using the 3D-HP lattice model

The problem of protein structure prediction in the hydrophobic-polar (HP) lattice model is the prediction of protein tertiary structure. This problem is usually referred to as the protein folding problem. This paper presents a method for the application of an enhanced hybrid search algorithm to the problem of protein folding prediction, using the three dimensional (3D) HP lattice model. The enhanced hybrid search algorithm is a combination of the particle swarm optimizer (PSO) and tabu search (TS) algorithms. Since the PSO algorithm entraps local minimum in later evolution extremely easily, we combined PSO with the TS algorithm, which has properties of global optimization. Since the technologies of crossover and mutation are applied many times to PSO and TS algorithms, so enhanced hybrid search algorithm is called the MCMPSO-TS (multiple crossover and mutation PSO-TS) algorithm. Experimental results show that the MCMPSO-TS algorithm can find the best solutions so far for the listed benchmarks, which will help comparison with any future paper approach. Moreover, real protein sequences and Fibonacci sequences are verified in the 3D HP lattice model for the first time. Compared with the previous evolutionary algorithms, the new hybrid search algorithm is novel, and can be used effectively to predict 3D protein folding structure. With continuous development and changes in amino acids sequences, the new algorithm will also make a contribution to the study of new protein sequences.     

Biomimicry of symbiotic multi-species coevolution for discrete and continuous optimization in RFID networks

In recent years, symbiosis as a rich source of potential engineering applications and computational model has attracted more and more attentions in the adaptive complex systems and evolution computing domains. Inspired by different symbiotic coevolution forms in nature, this paper proposed a series of multi-swarm particle swarm optimizers called PS²Os, which extend the single population particle swarm optimization (PSO) algorithm to interacting multi-swarms model by constructing hierarchical interaction topologies and enhanced dynamical update equations. According to different symbiotic interrelationships, four versions of PS²O are initiated to mimic mutualism, commensalism, predation, and competition mechanism, respectively. In the experiments, with five benchmark problems, the proposed algorithms are proved to have considerable potential for solving complex optimization problems. The coevolutionary dynamics of symbiotic species in each PS²O version are also studied respectively to demonstrate the heterogeneity of different symbiotic interrelationships that effect on the algorithm’s performance. Then PS²O is used for solving the radio frequency identification (RFID) network planning (RNP) problem with a mixture of discrete and continuous variables. Simulation results show that the proposed algorithm outperforms the reference algorithms for planning RFID networks, in terms of optimization accuracy and computation robustness.     

Application of particle swarm optimization and proximal support vector machines for fault detection

This paper presents a novel application of particle swarm optimization (PSO) in combination with another computational intelligence (CI) technique, namely, proximal support vector machine (PSVM) for machinery fault detection. Both real-valued and binary PSO algorithms have been considered along with linear and nonlinear versions of PSVM. The time domain vibration signals of a rotating machine with normal and defective bearings are processed for feature extraction. The features extracted from original and preprocessed signals are used as inputs to the classifiers (PSVM) for detection of machine condition. Input features are selected using a PSO algorithm. The classifiers are trained with a subset of experimental data for known machine conditions and are tested using the remaining data. The procedure is illustrated using the experimental vibration data of a rotating machine. The influences of the number of features, PSO algorithms and type of classifiers (linear or nonlinear PSVM) on the detection success are investigated. Results are compared with a genetic algorithm (GA) and principal component analysis (PCA). The PSO based approach gave test classification success above 90% which were comparable with the GA and much better than PCA. The results show the effectiveness of the selected features and classifiers in detection of machine condition.     

Performance evaluation of TRIBES, an adaptive particle swarm optimization algorithm

This paper presents a study of the performance of TRIBES, an adaptive particle swarm optimization algorithm. Particle Swarm Optimization (PSO) is a biologically-inspired optimization method. Recently, researchers have used it effectively in solving various optimization problems. However, like most optimization heuristics, PSO suffers from the drawback of being greatly influenced by the selection of its parameter values. Thus, the common belief is that the performance of a PSO algorithm is directly related to the tuning of such parameters. Usually, such tuning is a lengthy, time consuming and delicate process. A new adaptive PSO algorithm called TRIBES avoids manual tuning by defining adaptation rules which aim at automatically changing the particles’ behaviors as well as the topology of the swarm. In TRIBES, the topology is changed according to the swarm behavior and the strategies of displacement are chosen according to the performances of the particles. A comparative study carried out on a large set of benchmark functions shows that the performance of TRIBES is quite competitive compared to most other similar PSO algorithms that need manual tuning of parameters. The performance evaluation of TRIBES follows the testing procedure introduced during the 2005 IEEE Conference on Evolutionary Computation. The main objective of the present paper is to perform a global study of the behavior of TRIBES under several conditions, in order to determine strengths and drawbacks of this adaptive algorithm.     

Wind power prediction based on neural network with optimization of adaptive multi-group salp swarm algorithm

Salp swarm algorithm (SSA) is a swarm intelligence algorithm inspired by the swarm behavior of salps in oceans. In this paper, a adaptive multi-group salp swarm algorithm (AMSSA) with three new communication strategies is presented. Adaptive multi-group mechanism is to evenly divide the initial population into several subgroups, and then exchange information among subgroups after each adaptive iteration. Communication strategy is also an important part of adaptive multi-group mechanism. This paper proposes three new communication strategies and focuses on promoting the performance of SSA. These measures significantly improve the cooperative ability of SSA, accelerate convergence speed, and avoid easily falling into local optimum. And the benchmark functions confirm that AMSSA is better than the original SSA in exploration and exploitation. In addition, AMSSA is combined with prediction of wind power based on back propagation (AMSSA-BP) neural network. The simulation results show that the AMSSA-BP neural network prediction model can achieve a better prediction effect of wind power.

     

Simulated Evolutionary Optimization and Local Search: Introduction and Application to Tree Search

Tree search and its more complicated variant, tree search and simultaneous multiple DNA sequence alignment, are difficult NP-complete optimization problems, which require the application of advanced computational techniques, if large data sets are to be solved within reasonable computation times. Traditionally tree search has been attacked with a search strategy that is best described as multistart hill-climbing; local search by branch swapping has been performed on several different starting trees. Recently a different tree search strategy was tested in the Parsigal parsimony program, which used a combination of evolutionary optimization and local search. Evolutionary optimization algorithms use principles adopted from biological evolution to solve technical optimization tasks. Evolutionary optimization is a stochastic global search method, which means that the method is able to escape local optima, and is in principle able to produce any solution in the search space (although this may take a long time). Local search techniques, such as branch swapping, employ a completely different search strategy; they exploit local information maximally in order to achieve quick improvement in the value of the objective function. However, local search algorithms lack the ability to escape from local optima, which is a fundamental requirement for any search algorithm that aims to be able to discover the global optimum of a multimodal optimization problem. Hence it seems that an optimization strategy combining the good properties of both evolutionary algorithms and local search would be ideal. In this study, aspects of global optimization and local search are discussed, and the method of simulated evolutionary optimization is reviewed in detail. The application of simulated evolutionary optimization to tree search in Parsigal is then reviewed briefly.     

Self-adaptive particle swarm optimization: a review and analysis of convergence

Particle swarm optimization (PSO) is a population-based, stochastic search algorithm inspired by the flocking behaviour of birds. The PSO algorithm has been shown to be rather sensitive to its control parameters, and thus, performance may be greatly improved by employing appropriately tuned parameters. However, parameter tuning is typically a time-intensive empirical process. Furthermore, a priori parameter tuning makes the implicit assumption that the optimal parameters of the PSO algorithm are not time-dependent. To address these issues, self-adaptive particle swarm optimization (SAPSO) algorithms adapt their control parameters throughout execution. While there is a wide variety of such SAPSO algorithms in the literature, their behaviours are not well understood. Specifically, it is unknown whether these SAPSO algorithms will even exhibit convergent behaviour. This paper addresses this lack of understanding by investigating the convergence behaviours of 18 SAPSO algorithms both analytically and empirically. This paper also empirically examines whether the adapted parameters reach a stable point and whether the final parameter values adhere to a well-known convergence criterion. The results depict a grim state for SAPSO algorithms; over half of the SAPSO algorithms exhibit divergent behaviour while many others prematurely converge.