Multi-Cohort Intelligence algorithm: an intra- and inter-group learning behaviour based socio-inspired optimisation methodology

ABSTRACT

A Multi-Cohort Intelligence (Multi-CI) metaheuristic algorithm in emerging socio-inspired optimisation domain is proposed. The algorithm implements intra-group and inter-group learning mechanisms. It focusses on the interaction amongst different cohorts. The performance of the algorithm is validated by solving 75 unconstrained test problems with dimensions up to 30. The solutions were comparing with several recent algorithms such as Particle Swarm Optimisation (PSO), Covariance Matrix Adaptation Evolution Strategy, Artificial Bee Colony, Self-Adaptive Differential Evolution Algorithm, Comprehensive Learning Particle Swarm Optimisation, Backtracking Search Optimisation Algorithm, and Ideology Algorithm. The Wilcoxon signed-rank test was carried out for the statistical analysis and verification of the performance. The proposed Multi-CI outperformed these algorithms in terms of the solution quality including objective function value and computational cost, i.e. computational time and functional evaluations. The prominent feature of the Multi-CI algorithm along with the limitations is discussed as well. In addition, an illustrative example is also solved and every detail is provided.     

用于求解对称旅行商问题的粒子群算法和蚂蚁算法的融合

近年来,基于仿生学的随机优化技术成为学术界研究的重点问题之一,并在许多领域得到应用。粒子群优化（PSO）算法和蚂蚁算法ACO（Ant Colong Optimization）是随机全局优化的两个重要方法。PSO算法初始收敛速度较快,但在接近最优解时,收敛速度较慢,而ACO正好相反。结合二者的优势,先利用粒子群算法,再结合蚂蚁算法,以对称旅行商问题为例进行了仿真实现。实验结果表明,先利用PSO算法进行初步求解,在利用蚂蚁算法进行精细求解,可以得到较好的效果。     

Position control of a quadcopter drone using evolutionary algorithms-based self-tuning for first-order Takagi–Sugeno–Kang fuzzy logic autopilots

Trajectory tracking control of a quadcopter drone is a challenging work due to highly-nonlinear dynamics of the system, coupled with uncertainties in the flight environment (e.g. unpredictable wind gusts, measurement noise, modelling errors, etc). This paper addresses the aforementioned research challenges by proposing evolutionary algorithms-based self-tuning for first-order Takagi–Sugeno–Kang-type fuzzy logic controller (FLC). We consider three major state-of-the-art optimisation algorithms, namely, Genetic Algorithm, Particle Swarm Optimisation, and Artificial Bee Colony to facilitate automatic tuning. The effectiveness of the proposed control schemes is tested and compared under several different flight conditions, such as, constant, varying step and sine functions. The results show that the ABC-FLC outperforms the GA-FLC and PSO-FLC in terms of minimising the settling time in the absence of overshoots.     

Transit network design by Bee Colony Optimization

The transit network design problem is one of the most significant problems faced by transit operators and city authorities in the world. This transportation planning problem belongs to the class of difficult combinatorial optimization problem, whose optimal solution is difficult to discover. The paper develops a Swarm Intelligence (SI) based model for the transit network design problem. When designing the transit network, we try to maximize the number of satisfied passengers, to minimize the total number of transfers, and to minimize the total travel time of all served passengers. Our approach to the transit network design problem is based on the Bee Colony Optimization (BCO) metaheuristics. The BCO algorithm is a stochastic, random-search technique that belongs to the class of population-based algorithms. This technique uses a similarity among the way in which bees in nature look for food, and the way in which optimization algorithms search for an optimum of a combinatorial optimization problem. The numerical experiments are performed on known benchmark problems. We clearly show that our approach, based on the BCO algorithm, is competitive with other approaches in the literature, and it can generate high-quality solutions.     

结合头脑风暴优化的混合蚁群优化算法

特征选择能够有效提升数据分类的性能。为了进一步提升蚁群优化（ACO）在特征选择上的求解能力,提出一种结合头脑风暴优化的混合蚁群优化（ABO）算法。该算法利用信息交流档案维护历史较好解,并通过基于松弛因子的时间最久优先方法动态更新档案。当ACO的全局最优解多次未更新时,采用基于Fuch混沌映射方法的路径-想法转换算子将档案中的路径解转换为想法解,并将其作为初始种群,通过头脑风暴优化（BSO）在更广阔的空间中搜索较好解。对所提算法在6组典型的二分类数据集上进行实验,分析了其参数敏感性,并与混合萤火虫粒子群优化（HFPSO）算法、粒子群优化与引力搜索算法（PSOGSA）以及遗传算法（GA） 这三种典型的演化算法进行对比。实验结果表明,相较于对比算法,所提算法在分类正确率上至少可提高2.88%~5.35%,在F1指标上至少可提高0.02~0.05,验证了所提算法的有效性和优越性。     

A review of ant algorithms

Ant algorithms are optimisation algorithms inspired by the foraging behaviour of real ants in the wild. Introduced in the early 1990s, ant algorithms aim at finding approximate solutions to optimisation problems through the use of artificial ants and their indirect communication via synthetic pheromones. The first ant algorithms and their development into the Ant Colony Optimisation (ACO) metaheuristic is described herein. An overview of past and present typical applications as well as more specialised and novel applications is given. The use of ant algorithms alongside more traditional machine learning techniques to produce robust, hybrid, optimisation algorithms is addressed, with a look towards future developments in this area of study.     

Tabu Search with two approaches to parallel flowshop evaluation on CUDA platform

The introduction of NVidia’s powerful Tesla GPU hardware and Compute Unified Device Architecture (CUDA) platform enable many-core parallel programming. As a result, existing algorithms implemented on a GPU can run many times faster than on modern CPUs. Relatively little research has been done so far on GPU implementations of discrete optimisation algorithms. In this paper, two approaches to parallel GPU evaluation of the Permutation Flowshop Scheduling Problem, with makespan and total flowtime criteria, are proposed. These methods can be employed in most population-based algorithms, e.g. genetic algorithms, Ant Colony Optimisation, Particle Swarm Optimisation, and Tabu Search. Extensive computational experiments, on Tabu Search for Flowshop with both criteria, followed by statistical analysis, confirm great computational capabilities of GPU hardware. A GPU implementation of Tabu Search runs up to 89 times faster than its CPU counterpart.     

Landscape analysis for multicast routing

In recent years, methods based on local search have been utilized in off-line algorithms for multicast routing in communication networks. In multicast routing, several point-to-multipoint requests have to be scheduled under constraints associated with each link of the underlying network such as capacity and transmission costs. We propose a landscape analysis technique to solve this problem, where each particular request is routed by a Steiner tree heuristic. The landscape analysis employs simulated annealing with a logarithmic cooling schedule, which has been proved to be an appropriate tool to tackle hard problems from Combinatorial Optimisation and Computational Biology. We present results from computational experiments performed on three instances from the OR library. Emphasis is placed on algorithmic aspects of off-line routing, in particular, on approximations of optimal parameter settings, rather than on implementations of fast online network protocols. Furthermore, we investigate the impact of capacity noise on parameter settings and we perform a comparison to genetic algorithms based upon PMX crossover.     

A survey on computational intelligence approaches for predictive modeling in prostate cancer

Predictive modeling in medicine involves the development of computational models which are capable of analysing large amounts of data in order to predict healthcare outcomes for individual patients. Computational intelligence approaches are suitable when the data to be modelled are too complex for conventional statistical techniques to process quickly and efficiently. These advanced approaches are based on mathematical models that have been especially developed for dealing with the uncertainty and imprecision which is typically found in clinical and biological datasets. This paper provides a survey of recent work on computational intelligence approaches that have been applied to prostate cancer predictive modeling, and considers the challenges which need to be addressed. In particular, the paper considers a broad definition of computational intelligence which includes metaheuristic optimisation algorithms (also known as nature inspired algorithms), Artificial Neural Networks, Deep Learning, Fuzzy based approaches, and hybrids of these, as well as Bayesian based approaches, and Markov models. Metaheuristic optimisation approaches, such as the Ant Colony Optimisation, Particle Swarm Optimisation, and Artificial Immune Network have been utilised for optimising the performance of prostate cancer predictive models, and the suitability of these approaches are discussed.     

Swarm intelligence and the quest to solve a garbage and recycling collection problem

This work focuses on the application of Swarm Intelligence to a problem of garbage and recycling collection using a swarm of robots. Computational algorithms inspired by nature, such as Particle Swarm Optimization (PSO) and Ant Colony Optimization, have been successfully applied to a range of optimization problems. Our idea is to train a number of robots to interact with each other, attempting to simulate the way a collective of animals behave, as a single cognitive entity. What we have achieved is a swarm of robots that interacts like a swarm of insects, cooperating with each other accurately and efficiently. We describe two different PSO topologies implemented, showing the obtained results, a comparative evaluation, and an explanation of the rationale behind the choices of topologies that enhanced the PSO algorithm. Moreover, we describe and implement an Ant Colony Optimization (ACO) approach that presents an unusual grid implementation of a robot physical simulation. Hence, generating new concepts and discussions regarding the necessary modifications for the algorithm towards an improved performance. The ACO is then compared to the PSO results in order to choose the best algorithm to solve the proposed problem.     

Combining K-Means and K-Harmonic with Fish School Search Algorithm for data clustering task on graphics processing units

Data clustering is related to the split of a set of objects into smaller groups with common features. Several optimization techniques have been proposed to increase the performance of clustering algorithms. Swarm Intelligence (SI) algorithms are concerned with optimization problems and they have been successfully applied to different domains. In this work, a Swarm Clustering Algorithm (SCA) is proposed based on the standard K-Means and on K-Harmonic Means (KHM) clustering algorithms, which are used as fitness functions for a SI algorithm: Fish School Search (FSS). The motivation is to exploit the search capability of SI algorithms and to avoid the major limitation of falling into locally optimal values of the K-Means algorithm. Because of the inherent parallel nature of the SI algorithms, since the fitness function can be evaluated for each individual in an isolated manner, we have developed the parallel implementation on GPU of the SCAs, comparing the performances with their serial implementation. The interest behind proposing SCA is to verify the ability of FSS algorithm to deal with the clustering task and to study the difference of performance of FSS-SCA implemented on CPU and on GPU. Experiments with 13 benchmark datasets have shown similar or slightly better quality of the results compared to standard K-Means algorithm and Particle Swarm Algorithm (PSO) algorithm. There results of using FSS for clustering are promising.     

Multi-objective Swarm Intelligence schedulers for online scientific Clouds

Cloud Computing is a promising paradigm for parallel computing. However, as Cloud-based services become more dynamic, resource provisioning in Clouds becomes more challenging. The paradigm, with its promise of virtually infinite resources, seems to suit well in solving resource greedy scientific computing problems. In a Cloud, an appropriate number of Virtual Machines (VM) is created and allocated in physical resources for executing jobs. This work focuses on the Infrastructure as a Service (IaaS) model where custom VMs are launched in appropriate hosts available in a Cloud to execute scientific experiments coming from multiple users. Finding optimal solutions to allocate VMs to physical resources is an NP-complete problem, and therefore many heuristics have been developed. In this work, we describe and evaluate two Cloud schedulers based on Swarm Intelligence (SI) techniques, particularly Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO). The main performance metrics to study are the number of serviced users by the Cloud and the total number of created VMs in online (non-batch) scheduling scenarios. We also perform a sensitivity analysis by varying the specific-parameter values of each algorithm to evaluate the impact on the performance of the two objective metrics. The intra-Cloud network traffic is also measured. Simulated experiments performed using CloudSim and job data from real scientific problems show that the use of SI-based techniques succeeds in balancing the studied metrics compared to Genetic Algorithms.     

Meta-modeling using generalized regression neural network and particle swarm optimization

Swarm intelligence (SI) and evolutionary computation (EC) algorithms are often used to solve various optimization problems. SI and EC algorithms generally require a large number of fitness function evaluations (i.e., higher computational requirements) to obtain quality solutions. This requirement becomes more challenging when optimization problems are associated with computationally expensive analyses and/or simulation tasks. To tackle this issue, meta-modeling has shown successful results in improving computational efficiency by approximating the fitness or constraint functions of these complex optimization problems. Meta-modeling approaches typically use polynomial regression, kriging, radial basis function network, and support vector machines. Less attention has been given to the generalized regression neural network approach, and yet, it offers several advantages. Specifically, the model construction process does not require iterations. Its only one parameter is known to be less sensitive and usually requires less effort in selecting an optimal parameter. We use generalized regression neural network in this paper to construct meta-models and to approximate the fitness function in particle swarm optimization. To assess the performance and quality of these solutions, the proposed meta-modeling approach is tested on ten benchmark functions. The results are promising in terms of the solution quality and computational efficiency, especially when compared against the results of particle swarm optimization without meta-modeling and several other meta-modeling methods in previously published literature.     

Benchmarking and comparison of nature-inspired population-based continuous optimisation algorithms

This paper describes an experimental investigation into four nature-inspired population-based continuous optimisation methods: the Bees Algorithm, Evolutionary Algorithms, Particle Swarm Optimisation, and the Artificial Bee Colony algorithm. The aim of the proposed study is to understand and compare the specific capabilities of each optimisation algorithm. For each algorithm, thirty-two configurations covering different combinations of operators and learning parameters were examined. In order to evaluate the optimisation procedures, twenty-five function minimisation benchmarks were designed by the authors. The proposed set of benchmarks includes many diverse fitness landscapes, and constitutes a contribution to the systematic study of optimisation techniques and operators. The experimental results highlight the strengths and weaknesses of the algorithms and configurations tested. The existence and extent of origin and alignment search biases related to the use of different recombination operators are highlighted. The analysis of the results reveals interesting regularities that help to identify some of the crucial issues in the choice and configuration of the search algorithms.     

A flexible distributed framework for realising electric and plug-in hybrid vehicle charging policies

Motivated by the problems of charging a number of electric vehicles via limited capacity infrastructure, this article considers the problem of individual load adjustment under a total capacity constraint. For reasons of scalability and simplified communications, distributed solutions to this problem are sought. Borrowing from communication networks (AIMD algorithms) and distributed convex optimisation, we describe a number of distributed algorithms for achieving relative average fairness whilst maximising utilisation. We present analysis and simulation results to show the performance of these algorithms. In the scenarios examined, the algorithm's performance is typically within 5% of that achievable in the ideal centralised case, but with greatly enhanced scalability and reduced communication requirements.     

A survey on nature-inspired optimization algorithms with fuzzy logic for dynamic parameter adaptation

Metaheuristic optimization algorithms have become a popular choice for solving complex problems which are otherwise difficult to solve by traditional methods. However, these methods have the problem of the parameter adaptation and many researchers have proposed modifications using fuzzy logic to solve this problem and obtain better results than the original methods. In this study a comprehensive review is made of the optimization techniques in which fuzzy logic is used to dynamically adapt some important parameters in these methods. In this paper, the survey mainly covers the optimization methods of Particle Swarm Optimization (PSO), Gravitational Search Algorithm (GSA), and Ant Colony Optimization (ACO), which in the last years have been used with fuzzy logic to improve the performance of the optimization methods.     

Parameter estimation for crop growth model using evolutionary and bio-inspired algorithms

All dynamic crop models for growth and development have several parameters whose values are usually determined by using measurements coming from the real system. The parameter estimation problem is raised as an optimization problem and optimization algorithms are used to solve it. However, because the model generally is nonlinear the optimization problem likely is multimodal and therefore classical local search methods fail in locating the global minimum and as a consequence the model parameters could be inaccurate estimated. This paper presents a comparison of several evolutionary (EAs) and bio-inspired (BIAs) algorithms, considered as global optimization methods, such as Differential Evolution (DE), Covariance Matrix Adaptation Evolution Strategy (CMA-ES), Particle Swarm Optimization (PSO) and Artificial Bee Colony (ABC) on parameter estimation of crop growth SUCROS (a Simple and Universal CROp Growth Simulator) model. Subsequently, the SUCROS model for potential growth was applied to a husk tomato crop (Physalis ixocarpa Brot. ex Horm.) using data coming from an experiment carried out in Chapingo, Mexico. The objective was to determine which algorithm generates parameter values that give the best prediction of the model. An analysis of variance (ANOVA) was carried out to statistically evaluate the efficiency and effectiveness of the studied algorithms. Algorithm's efficiency was evaluated by counting the number of times the objective function was required to approximate an optimum. On the other hand, the effectiveness was evaluated by counting the number of times that the algorithm converged to an optimum. Simulation results showed that standard DE/rand/1/bin got the best result.     

Enhancing the performance of hybrid genetic algorithms by differential improvement

A differential improvement modification to Hybrid Genetic Algorithms is proposed. The general idea is to perform more extensive improvement algorithms on higher quality solutions. Our proposed Differential Improvement (DI) approach is of rather general character. It can be implemented in many different ways. The paradigm remains invariant and can be easily applied to a wider class of optimization problems. Moreover, the DI framework can also be used within other Hybrid metaheuristics like Hybrid Scatter Search algorithms, Particle Swarm Optimization, or Bee Colony Optimization techniques.     

Measuring the convergence and diversity of CDAS Multi-Objective Particle Swarm Optimization Algorithms: A study of many-objective problems

The interest for many-objective optimization has grown due to the limitations of Pareto dominance based Multi-Objective Evolutionary Algorithms when dealing with problems of a high number of objectives. Recently, some many-objective techniques have been proposed to avoid the deterioration of these algorithms' search ability. At the same time, the interest in the use of Particle Swarm Optimization (PSO) algorithms in multi-objective problems also grew. The PSO has been found to be very efficient to solve multi-objective problems (MOPs) and several Multi-Objective Particle Swarm Optimization (MOPSO) algorithms have been proposed. This work presents a study of the behavior of MOPSO algorithms in many-objective problems. The many-objective technique named control of dominance area of solutions (CDAS) is used on two Multi-Objective Particle Swarm Optimization algorithms. An empirical analysis is performed to identify the influence of the CDAS technique on the convergence and diversity of MOPSO algorithms using three different many-objective problems. The experimental results are compared applying quality indicators and statistical tests.     

Frequency assignment problem in satellite communications using differential evolution

Satellite communications technology has a tremendous impact in refining our world. The frequency assignment problem is of a fundamental importance when it comes to providing high-quality transmissions in satellite communication systems. The NP-complete frequency assignment problem in satellite communications involves the rearrangement of frequencies of one set of carriers while keeping the other set fixed in order to minimize the largest and total interference among carriers. In this paper, we present a number of algorithms, based on differential evolution, to solve the frequency assignment problem. We investigate several schemes ranging from adaptive differential evolution to hybrid algorithms in which heuristic is embedded within differential evolution. The effectiveness and robustness of our proposed algorithms is demonstrated through solving a set of benchmark problems and comparing the results with a number of previously proposed techniques that solve the same problem. Experimental results show that our proposed algorithms, in general, and hybrid ones in particular, outperform the existing algorithms both in terms of the quality of the solutions and computational time.