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In current years, Biogeography-Based Optimization (BBO), a novel Evolutionary Algorithm (EA), has drawn a lot of attention due to its dramatic performance. In our previous work, BBO’s migration models for single-objective problem (SOP) have been investigated to reveal their effects to algorithm’s performance. However to date, there is a few investigation about migration models for Multi-Objective Problems (MOPs) which are common in practice though more difficult. To make BBO competitive in dealing with MOPs, migration models of BBO are explored and exploited in this paper. One contribution of our work is that we propose Multi-objective BBO (MOBBO). By comparing MOBBO with other popular MOEAs, this algorithm is competent to handle MOPs. Besides, we present and compare six principal migration models. In comparisons, we find that Trapezoidal Migration Model performs well for MOPs, while its performance is inferior to other migration models for SOPs. Besides, Quadratic Migration Model’s performance for MOPs is worse, while it has a good performance to solve SOPs. These demonstrate that the conclusion to evaluate migration models for SOPs does not hold for MOPs, so another contribution in this paper is that we reevaluate migration models for MOPs in an empirical way, which is helpful to design migration models for MOBBO. [ABSTRACT FROM AUTHOR]

This paper proposes using the opposition-based learning (OBL) strategy in the shuffled frog leaping (SFL). The SFL divides a population into several memeplexes and then improves each memeplex in an evolutionary process. The OBL by comparing the fitness of an individual to its opposite and retaining the fitter one in the population accelerates search process. The objective of this paper is to introduce new versions of the SFL which employ on one hand the OBL to accelerate the SFL without making premature convergence and on the other hand use the OBL strategy to diversify search moves of SFL. Four versions of SFL algorithm are proposed by incorporating the OBL and the SFL. All algorithms similarly use the opposition-based population initialization to achieve fitter initial individuals and their difference is in applying opposition-based generation jumping. Experiments are performed on parameter identification problems. The obtained results demonstrate that incorporating the opposition-based strategy and SFL performed in a proper way is a good idea to enhance performance of SFL. Two versions of opposition-based SFL outperform their pure competitor i.e., SFL in terms of all aspects on all problems but two other versions of SFL obtained a worse performance than the pure SFL. Also some performance comparisons of the proposed algorithms with some other algorithms reported in the literature confirm a significantly better performance of our proposed algorithms. Also in final part of the comparison study, a comparison of the proposed algorithm in this study in respect to other algorithms on CEC05 functions demonstrates a completely comparable performance of proposed algorithm. [ABSTRACT FROM AUTHOR]

The artificial bee colony (ABC) algorithm is a new branch of evolutionary algorithms (EAs) that is inspired by the collective foraging behavior of real honey bee colonies. Due to its foraging model and its solution search equation, ABC generally performs well in exploration but badly in exploitation. To address this concerning issue and obtain a good balance between exploration and exploitation, in this paper, we mainly introduce into the ABC an adaptive method for the population size (AMPS). AMPS is inspired by the natural principle that the size of a population is affected by the availability of food resources. When food resources are abundant, a population tends to expand; otherwise, a decrease in the availability of food resources leads to a shrinkage in the population size. Specifically, when the algorithm performs well in exploration, AMPS will shrink the population to enhance exploitation by periodically removing some inferior solutions that have low success rates. In contrast, AMPS will enlarge the population to improve exploration by introducing some reserved solutions. Furthermore, to make AMPS perform better, we design a new solution search equation for employed bees and onlooker bees. Moreover, we also improve the probability model of the onlooker bees. By embedding our three proposed algorithmic components into ABC, we propose a novel ABC variant, called APABC. To demonstrate the performance of APABC, we compare APABC with some state-of-the-art ABC variants and some other non-ABC methods on 22 scalable benchmark functions and 30 CEC2014 test functions. The simulation results show that APABC is better than or at least competitive with the competitors in terms of its solution quality, robustness and convergence speed. [ABSTRACT FROM AUTHOR]

Abstract: Biogeography-Based Optimization (BBO), inspired by the science of biogeography, is a novel population-based Evolutionary Algorithm (EA). For optimization problems, BBO builds the matching mathematical model of the organism distribution. In this evolutionary mechanism, species migrating among islands can be considered as the information transition among different solutions represented by habitats. Solutions are reassembled according to migration rates. However, so far, the migration models are generally designed by empirical studies. This leads to immature conclusions that are unreliable. To complete the previous works, this paper investigates transition probability matrices of BBO to clarify that the transition probability of median number of species is not the only determinant factor to influence performance. The impact of migration rates on BBO is mathematically discussed, which is helpful to design migration models. Using numerical simulations, the BBO and several other classical evolutionary algorithms are compared. The simulations also comprehensively explain the effect of the BBO’s properties on its performance including dimension, population size, and migration models. The results validate the theoretical analysis in this paper. [Copyright &y& Elsevier]

Abstract: One of the objectives of Evolutionary Computation (EC) has been to understand the processes of natural evolution and then model them algorithmically. Hans-Paul Schwefel, in his 1997 paper on the future challenges for EC argues that the more an algorithm models natural evolution at work in the universe, the better it will perform (even in terms of function optimization). There is enough data to suggest that slight differences in the understanding of the natural evolution can cause the associated Evolutionary Algorithms (EA) to change characteristically. The present paper tests Schwefel’s hypothesis against Charles Sanders Peirce’s theory which places semiotics, the theory of signs, at the heart of universal evolution. This course is followed because of three primary reasons. Firstly, Peirce has not been seriously tested in EC, although there have been EA based on other theories and sub-theories. Secondly, Peirce’s universal theory, by not being restricted to biological evolution alone, qualifies for Schwefel’s hypothesis, perhaps more than most other theories that have already been modeled algorithmically. But most importantly because, in experimental terms, it warrants an original claim that Peirce’s insights are useful in improving the existing EA in computer science, as Peircean EA can potentially solve some of the major problems in this area such as the loss of diversity, stagnation, or premature convergence. This paper provides a novel framework and consequently a simple algorithm based on Peirce’s theory of evolution, and tests it extensively against a benchmark set of mathematical problems of varying dimensions and complexity. Comparative results with classical and advanced EA form another significant part of the paper, and help in strengthening the viability of Schwefel–Peirce hypothesis for EC. [Copyright &y& Elsevier]

In the past decades, different evolutionary optimization methodologies have been proposed by scholars and exploited by practitioners, in a wide range of applications. Each paradigm shows distinctive features, typical advantages, and characteristic disadvantages; however, one single problem is shared by almost all of them: the “lack of speciation”. While natural selection favors variations toward greater divergence, in artificial evolution candidate solutions do homologize. Many authors argued that promoting diversity would be beneficial in evolutionary optimization processes, and that it could help avoiding premature convergence on sub-optimal solutions. The paper surveys the research in this area up to mid 2010s, it re-orders and re-interprets different methodologies into a single framework, and proposes a novel three-axis taxonomy. Its goal is to provide the reader with a unifying view of the many contributions in this important corpus, allowing comparisons and informed choices. Characteristics of the different techniques are discussed, and similarities are highlighted; practical ways to measure and promote diversity are also suggested. [ABSTRACT FROM AUTHOR]

This paper presents a novel evolutionary predator and prey strategy (EPPS) for global optimization. The EPPS is based on a dynamic predator-prey model stemmed from the study of animal group living behaviors. In the model, concepts of experienced predators, strategic predators, the prey and its safe location are developed to simulate three typical animal behaviors: scanning, hunting and escaping. To validate the applicability and practicability of EPPS, experiments were undertaken on a set of 20 benchmark functions and three real-world problems, respectively. The results show that EPPS has a more superior performance in comparison with other recently developed methods reported in the literature. [ABSTRACT FROM AUTHOR]

This paper considers the containment control of networked autonomous underwater vehicles guided by multiple dynamic leaders over a directed network. Each vehicle is subject to model uncertainty and unknown time-varying ocean disturbances. A new predictor-based neural dynamic surface control design approach is presented to develop the adaptive containment controllers, under which the trajectories of vehicles converge to the convex hull spanned by those of the leaders. Specifically, iterative neural updating laws, based on prediction errors, are constructed, which enable the accurate identification of the unknown dynamics for each vehicle, not only in steady state but also in transient state. Furthermore, this result is extended to the output-feedback case where only the position-yaw information can be measured. A neural observer is developed to recover the unmeasured velocity information. Based on the observed velocities of neighboring vehicles, distributed output-feedback containment controllers are devised, under which the containment can be achieved regardless of model uncertainty, unknown ocean disturbances, and unmeasured velocity information. For both cases, Lyapunov–Krasovskii functionals are used to prove the uniform ultimate boundedness of the closed-loop error signals. Comparative studies are given to show the performance improvement of the proposed methods. [ABSTRACT FROM AUTHOR]

In a market-oriented service computing environment, both back-end SLA (service level agreement) offers and front-end SLA requirements should be considered when performing service composition. In this paper, we address the optimization problem of SLA-constrained service composition and focus on the following issues: the difficulties related to preference definition and to weight assignment, the limitation of linear utility functions in identifying preferred skyline solutions, and the efficiency and scalability requirements of the optimization algorithm. We present a systematic approach based on a fuzzy preference model and on evolutionary algorithms. Specifically, we first model this multi-objective optimization problem using the weighted Tchebycheff distance rather than a linear utility function. We then present a fuzzy preference model for preference representation and weight assignment. In the model, a set of fuzzy linguistic preference terms and their properties are introduced for establishing consistent preference order of multiple QoS dimensions, and a weighting procedure is proposed to transform the preference into numeric weights. Finally, we present two evolutionary algorithms, i.e., single_EA and hybrid_EA, that implement different optimization objectives and that can be used in different SLA management scenarios for service composition. We conduct a set of experimental studies to evaluate the effectiveness of the proposed algorithms in determining the optimal solutions, and to evaluate their efficiency and scalability for different problem scales. [ABSTRACT FROM AUTHOR]

Dynamic flexible job shop scheduling is of significant importance to the implementation of real-world manufacturing systems. In order to capture the dynamic and multi-objective nature of flexible job shop scheduling, and provide different trade-offs among objectives, this paper develops a multi-objective evolutionary algorithm (MOEA)-based proactive–reactive method. The novelty of our method is that it is able to handle multiple objectives including efficiency and stability simultaneously, adapt to the new environment quickly by incorporating heuristic dynamic optimization strategies, and deal with two scheduling policies of machine assignment and operation sequencing together. Besides, a new mathematical model for the multi-objective dynamic flexible job shop scheduling problem (MODFJSSP) is constructed. With the aim of selecting one solution that fits into the decision maker’s preferences from the trade-off solution set found by MOEA, a dynamic decision making procedure is designed. Experimental results in a simulated dynamic flexible job shop show that our method can achieve much better performances than combinations of existing scheduling rules. Three MOEA-based rescheduling methods are compared. The modified ɛ -MOEA has the best overall performance in dynamic environments, and its computational time is much less than two others (i.e., NSGA-II and SPEA2). Utilities of introducing the stability objective, heuristic initialization strategies and the decision making approach are also validated. [ABSTRACT FROM AUTHOR]

Abstract: In this paper an interactive method for modeling the preferences of a Decision-Maker (DM) is employed to guide a modified version of the NSGA-II algorithm: the Interactive Non-dominated Sorting algorithm with Preference Model (INSPM). The INSPM’s task is to find a non-uniform sampling of the Pareto-optimal front with a detailed sampling of the DM’s preferred regions and a coarse sampling of the non-preferred regions. In the proposed technique, a Radial Basis Function (RBF) network is employed to construct a function which represents the DM’s utility function using ordinal information only, extracted from queries to the DM. The INSPM algorithm calls the DM’s preference model via a Dynamic Crowding Distance (DCD) density control method which provides the mechanism for increasing the sampling in the preferred regions and for decreasing it in non-preferred regions which allows a fine-tunning control of the Pareto-optimal front sampling density. [Copyright &y& Elsevier]

Abstract: Previous theoretical analyses of evolutionary multi-objective optimization (EMO) mostly focus on obtaining -approximations of Pareto fronts. However, in practical applications, an appropriate value of is critical but sometimes, for a multi-objective optimization problem (MOP) with unknown attributes, difficult to determine. In this paper, we propose a new definition for the finite representation of the Pareto front—the adaptive Pareto front, which can automatically accommodate the Pareto front. Accordingly, it is more practical to take the adaptive Pareto front, or its -approximation (termed the -adaptive Pareto front) as the goal of an EMO algorithm. We then perform a runtime analysis of a ( ) multi-objective evolutionary algorithm (( ) MOEA) for three MOPs, including a discrete MOP with a polynomial Pareto front (denoted as a polynomial DMOP), a discrete MOP with an exponential Pareto front (denoted as an exponential DMOP) and a simple continuous two-objective optimization problem (SCTOP). By employing an estimator-based update strategy in the ( ) MOEA, we show that (1) for the polynomial DMOP, the whole Pareto front can be obtained in the expected polynomial runtime by setting the population size equal to the number of Pareto vectors; (2) for the exponential DMOP, the expected polynomial runtime can be obtained by keeping increasing in the same order as that of the problem size n; and (3) the diversity mechanism guarantees that in the expected polynomial runtime the MOEA can obtain an -adaptive Pareto front of SCTOP for any given precision . Theoretical studies and numerical comparisons with NSGA-II demonstrate the efficiency of the proposed MOEA and should be viewed as an important step toward understanding the mechanisms of MOEAs. [Copyright &y& Elsevier]

Abstract: Locality has long been seen as a crucial property for the efficiency of Evolutionary Algorithms in general, and Genetic Programming (GP) in particular. A number of studies investigating the effects of locality in GP can be found in the literature. The majority of the previous research on locality focuses on syntactic aspects, and operator semantic locality has not been thoroughly tested. In this paper, we investigate the role of semantic locality of crossover in GP. We follow McPhee in measuring the semantics of a subtree using the fitness cases. We use this to define a semantic distance metric. This semantic distance supports the design of some new crossover operators, concentrating on improving semantic locality. We study the impact of these semantically based crossovers on the behaviour of GP. The results show substantial advantages accruing from the use of semantic locality. [Copyright &y& Elsevier]

Abstract: This paper proposes a novel and unconventional Memetic Computing approach for solving continuous optimization problems characterized by memory limitations. The proposed algorithm, unlike employing an explorative evolutionary framework and a set of local search algorithms, employs multiple exploitative search within the main framework and performs a multiple step global search by means of a randomized perturbation of the virtual population corresponding to a periodical randomization of the search for the exploitative operators. The proposed Memetic Computing approach is based on a populationless (compact) evolutionary framework which, instead of processing a population of solutions, handles its statistical model. This evolutionary framework is based on a Differential Evolution which cooperatively employs two exploitative search operators: the first is based on a standard Differential Evolution mutation and exponential crossover, and the second is the trigonometric mutation. These two search operators have an exploitative action on the algorithmic framework and thus contribute to the rapid convergence of the virtual population towards promising candidate solutions. The action of these search operators is counterbalanced by a periodical stochastic perturbation of the virtual population, which has the role of “disturbing” the excessively exploitative action of the framework and thus inhibits its premature convergence. The proposed algorithm, namely Disturbed Exploitation compact Differential Evolution, is a simple and memory-wise cheap structure that makes use of the Memetic Computing paradigm in order to solve complex optimization problems. The proposed approach has been tested on a set of various test problems and compared with state-of-the-art compact algorithms and with some modern population based meta-heuristics. Numerical results show that Disturbed Exploitation compact Differential Evolution significantly outperforms all the other compact algorithms present in literature and reaches a competitive performance with respect to modern population algorithms, including some memetic approaches and complex modern Differential Evolution based algorithms. In order to show the potential of the proposed approach in real-world applications, Disturbed Exploitation compact Differential Evolution has been implemented for performing the control of a space robot by simulating the implementation within the robot micro-controller. Numerical results show the superiority of the proposed algorithm with respect to other modern compact algorithms present in literature. [Copyright &y& Elsevier]

Abstract: Model trees are a particular case of decision trees employed to solve regression problems. They have the advantage of presenting an interpretable output, helping the end-user to get more confidence in the prediction and providing the basis for the end-user to have new insight about the data, confirming or rejecting hypotheses previously formed. Moreover, model trees present an acceptable level of predictive performance in comparison to most techniques used for solving regression problems. Since generating the optimal model tree is an NP-Complete problem, traditional model tree induction algorithms make use of a greedy top-down divide-and-conquer strategy, which may not converge to the global optimal solution. In this paper, we propose a novel algorithm based on the use of the evolutionary algorithms paradigm as an alternate heuristic to generate model trees in order to improve the convergence to globally near-optimal solutions. We call our new approach evolutionary model tree induction (E-Motion). We test its predictive performance using public UCI data sets, and we compare the results to traditional greedy regression/model trees induction algorithms, as well as to other evolutionary approaches. Results show that our method presents a good trade-off between predictive performance and model comprehensibility, which may be crucial in many machine learning applications. [Copyright &y& Elsevier]

Abstract: Motivated by the migration mechanisms of ecosystems, various extensions to biogeography-based optimization (BBO) are proposed here. As a global optimization method, BBO is an original algorithm based on the mathematical model of organism distribution in biological systems. BBO is an evolutionary process that achieves information sharing by biogeography-based migration operators. In BBO, habitats represent candidate problem solutions, and species migration represents the sharing of features between candidate solutions according to the fitness of the habitats. This paper generalizes equilibrium species count results in biogeography theory, explores the behavior of six different migration models in BBO, and investigates performance through 23 benchmark functions with a wide range of dimensions and diverse complexities. The performance study shows that sinusoidal migration curves provide the best performance among the six different models that we explored. In addition, comparison with other biology-based optimization algorithms is investigated, and the influence of the population size, problem dimension, mutation rate, and maximum migration rate of BBO are also studied. [Copyright &y& Elsevier]