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Multiple solutions are often needed because of different kinds of uncertain failures in a plan execution process and scenarios for which precise mathematical models and constraints are difficult to obtain. This paper proposes an optimization strategy for multirobot task allocation (MRTA) problems and makes efforts on offering multiple solutions with same or similar quality for switching and selection. Since the mentioned problem can be regarded as a multimodal optimization one, this paper presents a niching immune-based optimization algorithm based on Softmax regression (sNIOA) to handle it. A prejudgment of population is done before entering an evaluation process to reduce the evaluation time and to avoid unnecessary computation. Furthermore, a guiding mutation (GM) operator inspired by the base pair in theory of gene mutation is introduced into sNIOA to strengthen its search ability. When a certain gene mutates, the others in the same gene group are more likely to mutate with a higher probability. Experimental results show the improvement of sNIOA on the aspect of accelerating computation speed with comparison to other heuristic algorithms. They also show the effectiveness of the proposed GM operator by comparing sNIOA with and without it. Two MRTA application cases are tested finally. [ABSTRACT FROM AUTHOR]

When solving constrained optimization problems (COPs) by evolutionary algorithms, the search algorithm plays a crucial role. In general, we expect that the search algorithm has the capability to balance not only diversity and convergence but also constraints and objective function during the evolution. For this purpose, this paper proposes a composite differential evolution (DE) for constrained optimization, which includes three different trial vector generation strategies with distinct advantages. In order to strike a balance between diversity and convergence, one of these three trial vector generation strategies is able to increase diversity, and the other two exhibit the property of convergence. In addition, to accomplish the tradeoff between constraints and objective function, one of the two trial vector generation strategies for convergence is guided by the individual with the least degree of constraint violation in the population, and the other is guided by the individual with the best objective function value in the population. After producing offspring by the proposed composite DE, the feasibility rule and the $\boldsymbol {\varepsilon }$ constrained method are combined elaborately for selection in this paper. Moreover, a restart scheme is proposed to help the population jump out of a local optimum in the infeasible region for some extremely complicated COPs. By assembling the above techniques together, a constrained composite DE is proposed. The experiments on two sets of benchmark test functions with various features, i.e., 24 test functions from IEEE CEC2006 and 18 test functions with 10 dimensions and 30 dimensions from IEEE CEC2010, have demonstrated that the proposed method shows better or at least competitive performance against other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Population dynamics have been widely used in the design of learning and control systems for networked engineering applications, where the information dependency among elements of the network has become a relevant issue. Classic population dynamics (e.g., replicator, logit choice, Smith, and projection) require full information to evolve to the solution (Nash equilibrium). The main reason is that classic population dynamics are deduced by assuming well-mixed populations, which limits the applications where this theory can be implemented. In this paper, we extend the concept of population dynamics for nonwell-mixed populations in order to deal with distributed information structures that are characterized by noncomplete graphs. Although the distributed population dynamics proposed in this paper use partial information, they preserve similar characteristics and properties of their classic counterpart. Specifically, we prove mass conservation and convergence to Nash equilibrium. To illustrate the performance of the proposed dynamics, we show some applications in the solution of optimization problems, classic games, and the design of distributed controllers. [ABSTRACT FROM PUBLISHER]

This paper proposes a united-based imperialist competitive algorithm (UICA) for compensatory neural fuzzy systems. The original imperialist competitive algorithm (ICA) comprises numerous empires in the population, and each empire comprises one imperialist and some colonies. Each country represents a feasible solution in the empire, and the more favorable solutions become imperialists, taking over less favorable solutions (i.e., colonies). In the ICA, each colony moves toward its relevant imperialist according to an assimilation policy. This paper proposes a UICA that focuses on this assimilation policy to explore the characteristics of the colonies. The assimilation policy consists of three major parts in this paper. In the first part, a colony searches for the best previous position of the colony. In the second part, the colony faces the best-so-far imperialist. In the third part, the colony moves toward the corresponding imperialist of the colony. The proposed UICA was applied to nonlinear system problems, and the experimental results indicated that the proposed UICA is effective. [ABSTRACT FROM PUBLISHER]

In this paper, we introduce a novel method for the discovery of value functions for Markov decision processes (MDPs). This method, which we call value function discovery (VFD), is based on ideas from the evolutionary algorithm field. VFDs key feature is that it discovers descriptions of value functions that are algebraic in nature. This feature is unique, because the descriptions include the model parameters of the MDP. The algebraic expression of the value function discovered by VFD can be used in several scenarios, e.g., conversion to a policy (with one-step policy improvement) or control of systems with time-varying parameters. The work in this paper is a first step toward exploring potential usage scenarios of discovered value functions. We give a detailed description of VFD and illustrate its application on an example MDP. For this MDP, we let VFD discover an algebraic description of a value function that closely resembles the optimal value function. The discovered value function is then used to obtain a policy, which we compare numerically to the optimal policy of the MDP. The resulting policy shows near-optimal performance on a wide range of model parameters. Finally, we identify and discuss future application scenarios of discovered value functions. [ABSTRACT FROM PUBLISHER]

Pareto dominance-based multiobjective optimization has been successfully applied to constrained evolutionary optimization during the last two decades. However, as another famous multiobjective optimization framework, decomposition-based multiobjective optimization has not received sufficient attention from constrained evolutionary optimization. In this paper, we make use of decomposition-based multiobjective optimization to solve constrained optimization problems (COPs). In our method, first of all, a COP is transformed into a biobjective optimization problem (BOP). Afterward, the transformed BOP is decomposed into a number of scalar optimization subproblems. After generating an offspring for each subproblem by differential evolution, the weighted sum method is utilized for selection. In addition, to make decomposition-based multiobjective optimization suit the characteristics of constrained evolutionary optimization, weight vectors are elaborately adjusted. Moreover, for some extremely complicated COPs, a restart strategy is introduced to help the population jump out of a local optimum in the infeasible region. Extensive experiments on three sets of benchmark test functions, namely, 24 test functions from IEEE CEC2006, 36 test functions from IEEE CEC2010, and 56 test functions from IEEE CEC2017, have demonstrated that the proposed method shows better or at least competitive performance against other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Differential evolution is one of the most powerful evolutionary algorithms for single objective optimization problems in the literature. Its application in the multiobjective optimization problems is also very successful, and many kinds of promising multiobjective differential evolution (MODE) algorithms have been proposed in the literature. This paper develops a new variant of MODE with two features. First, a set of personal archives are maintained to evolve the search process instead of a population with a fixed size, and a truncation procedure is used to enhance selection pressure. Second, multiple biased mutation operators incorporating the target solution quality are proposed, and an adaptive selection method is adopted to allocate the mutation operators to solutions. The proposed MODE is referred to as the MODE with personal archive and biased self-adaptive mutation selection (BiasMOSaDE). A set of 31 benchmark multiobjective problems selected from the literature are adopted to evaluate its performance. Computational results illustrate that the proposed BiasMOSaDE is competitive or even superior to several state-of-the-art MODEs in the literature. [ABSTRACT FROM AUTHOR]

Genetic programming (GP) is a powerful evolutionary algorithm that has been widely used for solving many real-world optimization problems. However, traditional GP can only solve a single task in one independent run, which is inefficient in cases where multiple tasks need to be solved at the same time. Recently, multifactorial optimization (MFO) has been proposed as a new evolutionary paradigm toward evolutionary multitasking. It intends to conduct evolutionary search on multiple tasks in one independent run. To enable multitasking GP, in this paper, we propose a novel multifactorial GP (MFGP) algorithm. To the best of our knowledge, this is the first attempt in the literature to conduct multitasking GP using a single population. The proposed MFGP consists of a novel scalable chromosome encoding scheme which is capable of representing multiple solutions simultaneously, and new evolutionary mechanisms for MFO based on self-learning gene expression programming. Further, comprehensive experimental studies are conducted on multitask scenarios consisting of commonly used GP benchmark problems and real world applications. The obtained empirical results confirmed the efficacy of the proposed MFGP. [ABSTRACT FROM AUTHOR]

Nonlinear equations system (NES) arises commonly in science and engineering. Repulsion techniques are considered to be the effective methods to locate different roots of NES. In general, the repulsive radius needs to be given by the user before the run. However, its optimal parameter setting is difficult and problem-dependent. To alleviate this drawback, in this paper, we first propose a dynamic repulsion technique, and then a general framework based on the dynamic repulsion technique and evolutionary algorithms (EAs) is presented to effectively solve NES. The major advantages of our framework are: 1) the repulsive radius is controlled dynamically during the evolutionary process; 2) multiple roots of NES can be simultaneously located in a single run; 3) the diversity of the population is preserved due to the population reinitialization; and 4) different repulsion techniques and different EAs can be readily integrated into this framework. To extensively evaluate the performance of our framework, we choose 42 problems with diverse features as the test suite. In addition, some representative differential evolution and particle swarm optimization variants are incorporated into the framework. Our method is also compared with other state-of-the-art methods. Experimental results indicate that the dynamic repulsion technique can improve the performance of the original repulsion technique with static repulsive radius. Moreover, the proposed method is able to yield better results compared with other methods. [ABSTRACT FROM AUTHOR]

As the mutation strategy and algorithmic parameters in differential evolution (DE) are sensitive to the problems being solved, a hot research topic is to adaptively control the strategy and parameters according to the requirements of the problem. In the literature, most adaptive DE use either historical experiences of the population or heuristic information of the individuals to promote adaptation. In this paper, we develop a novel variant of adaptive DE, utilizing both the historical experience and heuristic information for the adaptation. In this novel historical and heuristic DE (HHDE), each individual dynamically adjusts its mutation strategy and associated parameters not only by learning from previous successful experience of the whole population, but also according to heuristic information related with its own current state. These help the algorithm select a more suitable mutation strategy and determinate better parameters for each individual in different evolutionary stages. The performance of the proposed HHDE is extensively evaluated on 30 benchmark functions with different dimensions. Experimental results confirm the competitiveness of the proposed algorithm to a number of DE variants. [ABSTRACT FROM AUTHOR]

Decomposition of a multiobjective optimization problem (MOP) into several simple multiobjective subproblems, named multiobjective evolutionary algorithm based on decomposition (MOEA/D)-M2M, is a new version of multiobjective optimization-based decomposition. However, it fails to consider different contributions from each subproblem but treats them equally instead. This paper proposes a collaborative resource allocation (CRA) strategy for MOEA/D-M2M, named MOEA/D-CRA. It allocates computational resources dynamically to subproblems based on their contributions. In addition, an external archive is utilized to obtain the collaborative information about contributions during a search process. Experimental results indicate that MOEA/D-CRA outperforms its peers on 61% of the test cases in terms of three metrics, thereby validating the effectiveness of the proposed CRA strategy in solving MOPs. [ABSTRACT FROM AUTHOR]

In this paper, a new model describing the human immunodeficiency virus (HIV)-acquired immuno deficiency syndrome (AIDS) epidemic spread is proposed. The improvement with respect to the known models has been driven by recent results obtained from historical data collection and the suggestions given by the World Health Organization: the characteristics of the virus diffusion, mainly by body fluids, imply the trivial fact that wise behaviors of healthy subjects and fast timely recognition of a new positive diagnosis should reduce the spread quite fast. Therefore, the set of susceptible subjects is divided into two categories: the wise people that, suitably informed, avoid dangerous behaviors, and the ones that, with irresponsible acts, could get the infection. The set of infected subjects is constituted by people who are still not aware of being infected (and therefore are responsible of the HIV spread), along with the subjects aware of being infected by HIV or AIDS. Inspired by the international guidelines suggestions, three controls are introduced, aiming both at the prevention and at the cure: an informative campaign, a test campaign, and an HIV/AIDS therapy action. Among them, the core of the control effort is a fast HIV diagnosis. The equilibrium points, their stability, and the influences of the introduced inputs to the system behavior are studied, yielding to preliminary statements for prospective works on suitable control design approaches. [ABSTRACT FROM AUTHOR]

The hospital system is a complex service system in which patients and hospitals interact and make their decisions based on bounded rationality and information. In this paper, we develop a generative agent-based model to simulate the behavior of a hospital service system. Our model combines agent-based simulation and queueing models to mimic the hospital service processes. Our goal is to simulate and understand the growth and size distribution of hospitals. This simulation model includes agents for supply elements (i.e., hospitals with different resources and expansion strategies) and demand elements (i.e., patients with different preferences for their hospital selections) in a hospital service system. Three important questions are investigated: 1) what is the emergent size distribution of hospitals? 2) what key factors influence the size distribution? and 3) how sensitive is the size distribution to these key factors? Simulation results show that the size distribution is neither power law nor lognormal. Rather, the distribution is leptokurtic, and more skewed than normal but less skewed than lognormal. This result contradicts those in extant literature on the size distributions of human and natural systems, including cities, firms, power-grids, and citation network. We conduct a set of experiments to identify the generative mechanisms for the hospital size distribution and to test the robustness of the results. The model was validated empirically by using a U.S. hospital size dataset. [ABSTRACT FROM AUTHOR]

This paper presents a novel evolutionary strategy for multiobjective optimization in which a population’s evolution is guided by exploiting the geometric structure of its Pareto front. Specifically, the Pareto front of a particle population is regarded as a set of scattered points on which interpolation is performed using a geometric curve/surface model to construct a geometric parameter space. On this basis, the normal direction of this space can be obtained and the solutions located exactly in this direction are chosen as the guiding points. Then, the dominated solutions are processed by using a local optimization technique with the help of these guiding points. Particle populations can thus evolve toward optimal solutions with the guidance of such a geometric structure. The strategy is employed to develop a fast and robust algorithm based on correlation analysis for solving the optimization problems with more than three objectives. A number of computational experiments have been conducted to compare the algorithm to another three popular multiobjective algorithms. As demonstrated in the experiments, the proposed algorithm achieves remarkable performance in terms of the solutions obtained, robustness, and speed of convergence. [ABSTRACT FROM AUTHOR]

This paper introduces a Markov model for evolutionary algorithms (EAs) that is based on interactions among individuals in the population. This interactive Markov model has the potential to provide tractable models for optimization problems of realistic size. We propose two simple discrete optimization search strategies with population-proportion-based selection and a modified mutation operator. The probability of selection is linearly proportional to the number of individuals at each point of the search space. The mutation operator randomly modifies an entire individual rather than a single decision variable. We exactly model these optimization search strategies with interactive Markov models. We present simulation results to confirm the interactive Markov model theory. We show that genetic algorithms and biogeography-based optimization perform better with the addition of population-proportion-based selection on a set of real-world benchmarks. We note that many other EAs, both new and old, might be able to be improved with this addition, or modeled with this method. [ABSTRACT FROM PUBLISHER]

This paper proposes a novel optimization scheme by hybridizing an artificial bee colony optimizer (HABC) with a bee life-cycle mechanism, for both stationary and dynamic optimization problems. The main innovation of the proposed HABC is to develop a cooperative and population-varying scheme, in which individuals can dynamically shift their states of birth, foraging, death, and reproduction throughout the artificial bee colony life cycle. That is, the bee colony size can be adjusted dynamically according to the local fitness landscape during algorithm execution. This new characteristic of HABC helps to avoid redundant search and maintain diversity of population in complex environments. A comprehensive experimental analysis is implemented that the proposed algorithm is benchmarked against several state-of-the-art bio-inspired algorithms on both stationary and dynamic benchmarks. Then the proposed HABC is applied to the real-world applications including data clustering and image segmentation problems. Statistical analysis of all these tests highlights the significant performance improvement due to the life-cycle mechanism and shows that the proposed HABC outperforms the reference algorithms. [ABSTRACT FROM PUBLISHER]

Teaching–learning-based optimization (TLBO) is an intelligent optimization algorithm with relatively fewer parameters that should be determined in updating equations. For solving complex optimization problems, the local optima often appear in the evolution. To decrease the possibility of this phenomenon, a novel TLBO variant (EI-TLBO) with experience information (EI) and differential mutation is presented. In the method, neighborhood information (the best individual NTeacher and the mean individual NMean) of each learner’s neighbors is introduced to improve the exploration capability. The EI before the current iteration of each learner is introduced to make him or her accurately judge the learning behavior in future. In addition, instead of duplicate elimination to maintain the diversity of population at the end of each generation in the original TLBO, differential mutation is introduced to maintain the diversity of learners during the iterative learning process. The main contribution of this paper is to improve the convergence speed and accuracy by introducing neighborhood topology structure, EI, and differential mutation. The efficiency of the proposed algorithm is evaluated on 46 benchmark functions, among which 27 functions are selected from CEC2013. Its performance is compared with those of six other reported EAs. The results indicate that EI-TLBO algorithm can achieve superior performance. [ABSTRACT FROM PUBLISHER]

In this paper, a novel approach is proposed to improve the classification performance of a polynomial neural network (PNN). In this approach, the partial descriptions (PDs) are generated at the first layer based on all possible combinations of two features of the training input patterns of a dataset. The set of PDs from the first layer, the set of all input features, and a bias constitute the chromosome of the real-coded genetic algorithm (RCGA). A system of equations is solved to determine the values of the real coefficients of each chromosome of the RCGA for the training dataset with the mean classification accuracy (CA) as the fitness value of each chromosome. To adjust these values for unknown testing patterns, the RCGA is iterated in the usual manner using simple selection, crossover, mutation, and elitist selection. The method is tested extensively with the University of California, Irvine benchmark datasets by utilizing tenfold cross validation of each dataset, and the performance is compared with various well-known state-of-the-art techniques. The results obtained from the proposed method in terms of CA are superior and outperform other known methods on various datasets. [ABSTRACT FROM PUBLISHER]

Storing biometric templates and/or encryption keys, as adopted in traditional biometrics-based authentication methods, has raised a matter of serious concern. To address such a concern, biometric key generation, which derives encryption keys directly from statistical features of biometric data, has emerged to be a promising approach. Existing methods of this approach, however, are generally unable to appropriately model user variations, making them difficult to produce consistent and discriminative keys of high entropy for authentication purposes. This paper develops a semisupervised clustering scheme, which is optimized through a niching memetic algorithm, to effectively and simultaneously model both intra- and interuser variations. The developed scheme is employed to model the user variations on both single features and feature subsets with the purpose of recovering a large number of consistent and discriminative feature elements for key generation. Moreover, the scheme is designed to output a large number of clusters, thus further assisting in producing long while consistent and discriminative keys. Based on this scheme, a biometric key generation method is finally proposed. The performance of the proposed method has been evaluated on the biometric modality of handwritten signatures and compared with existing methods. The results show that our method can deliver consistent and discriminative keys of high entropy, outperforming-related methods. [ABSTRACT FROM AUTHOR]

The populations of industrialized countries will decline over the coming decades. To ensure that we have efficient access networks in the coming age of depopulation, it is important to consider the reorganization of central offices over time. The cost for operating and maintaining central offices can be reduced by efficiently closing central offices. However, dynamic facility location models that have been developed largely focused on the problem of optimally expanding a network over time to meet increasing demand. The practical relevance of the dynamic models is limited because the complexity and the required amount of data are much greater than with static models. This paper proposes a dynamic access network reorganization model for the depopulation age. Although the proposed model is a dynamic model, it is simple because it assumes that the only option will be to close central offices. The model first searches for a set of optimal solutions for each planning period as a capacitated facility location problem. Then, it selects the optimal combination of optimal solutions that can minimize the total number of open offices with the limitation of network reorganization. The validity of the proposed model was evaluated with computer simulations. It was confirmed that although the proposed model is a dynamic model it is simple and practical. [ABSTRACT FROM PUBLISHER]

Computational tools are needed to make data-driven disaster mitigation planning accessible to planners and policymakers without the need for programming or geographic information systems expertise. To address this problem, we have created modules to facilitate quantitative analyses pertinent to a variety of different disaster scenarios. These modules, which comprise the REsponse PLan ANalyzer framework, may be used to create tools for specific disaster scenarios that allow planners to harness large amounts of disparate data and execute computational models through a point-and-click interface. Bio-E, a user-friendly tool built using this framework, was designed to develop and analyze the feasibility of ad hoc clinics for treating populations following a biological emergency event. In this paper, the design and implementation of the RE-PLAN framework are described, and the functionality of the modules used in the Bio-E biological emergency mitigation tools are demonstrated. [ABSTRACT FROM AUTHOR]

This paper aims to design new strategies to extend traditional multiobjective optimization algorithms to efficiently obtain Pareto-optimal solutions in presence of noise on the objective surfaces. The first strategy, referred to as adaptive selection of sample size, is employed to balance the tradeoff between quality measure of fitness and run-time complexity. The second strategy is concerned with determining statistical expectation, instead of conventional averaging, of fitness samples as the measure of fitness of the trial solutions. The third strategy attempts to extend Goldberg's method to compare slightly worse trial solutions with its competitor by a more statistically viable comparator to examine possible placement of the former solution in the Pareto optimal front. The traditional differential evolution for multiobjective optimization algorithm has been modified by extending its selection step with the proposed strategies. Experiments undertaken to study the performance of the extended algorithm reveal that the extended algorithm outperforms its competitors with respect to three performance metrics, when examined on a test suite of 23 standard benchmarks with additive noise of three statistical distributions. The extended algorithm has been applied on the well known box-pushing problem, where the forces and torques required to shift the box by two robots are evaluated to jointly satisfy the conflicting objectives on task-execution time and energy consumption in presence of noise on range estimates from the sidewalls of the workspace. The application justifies the importance of the proposed noise-handling strategies in practical systems. [ABSTRACT FROM AUTHOR]

In the path to the future implementation of the smart grid, microgrids are presented as a cornerstone. An efficient and optimal microgrid operation is paramount. In this paper, we present a hierarchical microgrid management system using task sharing and an evolutionary game theory based dispatch strategy as a coordination algorithm to integrate the three main control levels needed for microgrid operation, which can be considered as a distributed intelligent system. The proposed algorithm shares the total load demanded into the distributed generators efficiently, while the microgrid utility is maximized dynamically, increasing the system reliability. Some simulation results are presented to illustrate the effectiveness of the dynamic performance of the proposed approach compared with a traditional hierarchical algorithm. [ABSTRACT FROM AUTHOR]