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[Display omitted] • Comparing to the other research papers, this paper compares the performance of the oldest, newest, more minor and well-known algorithms on deterministic chaos generators in one massive and unique study. • Paper show that by precision tuning, the original chaotic series convert into short N periodic time series (PTS). Thus no randomness as usually understand is there. These series are then used instead of classics pseudorandom numbers with positive impact. • Paper reveal the clearly visible positive impact of PTS on evolutionary algorithms (EAs) dynamics, which is visible almost on all algorithms used in this paper. Compared with the same EAs with classic random generators. • Paper open the question of whether standard random (nonchaotic) processes are really necessary for algorithm dynamics and suggest relations between randomness in EAs and noise in dynamical system control and theory. • Paper open, sketch and suggest new ideas and strategies on how to understand algorithm dynamics as the discrete feedback dynamical systems. Random mechanisms including mutations are an internal part of evolutionary algorithms, which are based on the fundamental ideas of Darwin's theory of evolution as well as Mendel's theory of genetic heritage. In this paper, we debate whether pseudo-random processes are needed for evolutionary algorithms or whether deterministic chaos, which is not a random process, can be suitably used instead. Specifically, we compare the performance of 10 evolutionary algorithms driven by chaotic dynamics and pseudo-random number generators using chaotic processes as a comparative study. In this study, the logistic equation is employed for generating periodical sequences of different lengths, which are used in evolutionary algorithms instead of randomness. We suggest that, instead of pseudo-random number generators, a specific class of deterministic processes (based on deterministic chaos) can be used to improve the performance of evolutionary algorithms. Finally, based on our findings, we propose new research questions. [ABSTRACT FROM AUTHOR]

With the ubiquitous of GPS-equipped devices, spatial crowdsourcing (SC) technology has been widely utilized in our daily life. As a novel computing paradigm, it hires mobile users as workers who physically move to the location of the task and perform the task. Task assignment is a fundamental issue in SC. In real life, there are many complex tasks requiring different workers, among which the quality of worker cooperation and the price satisfaction of users should not be ignored. Hence, this paper examines a satisfaction-aware task assignment (SATA) problem with the goal of maximizing overall user satisfaction, where the user satisfaction integrates the satisfaction towards price and cooperation quality. The SATA problem has been proved to be NP-hard by reducing it from the k -set packing problem. In addition, two algorithms, namely, conflict-aware greedy (CAG) algorithm and game theoretic (GT) algorithm with an optimization strategy, are proposed for solving the SATA problem. The CAG algorithm can efficiently obtain a result with provable approximate bound, while the GT algorithm is proven to be convergent which can find a Nash equilibrium. Extensive experiments have demonstrated the effectiveness and efficiency of our proposed approaches on both real and synthetic datasets. [ABSTRACT FROM AUTHOR]

With the ubiquitous of GPS-equipped devices, spatial crowdsourcing (SC) technology has been widely utilized in our daily life. As a novel computing paradigm, it hires mobile users as workers who physically move to the location of the task and perform the task. Task assignment is a fundamental issue in SC. In real life, there are many complex tasks requiring different workers, among which the quality of worker cooperation and the price satisfaction of users should not be ignored. Hence, this paper examines a satisfaction-aware task assignment (SATA) problem with the goal of maximizing overall user satisfaction, where the user satisfaction integrates the satisfaction towards price and cooperation quality. The SATA problem has been proved to be NP-hard by reducing it from the k -set packing problem. In addition, two algorithms, namely, conflict-aware greedy (CAG) algorithm and game theoretic (GT) algorithm with an optimization strategy, are proposed for solving the SATA problem. The CAG algorithm can efficiently obtain a result with provable approximate bound, while the GT algorithm is proven to be convergent which can find a Nash equilibrium. Extensive experiments have demonstrated the effectiveness and efficiency of our proposed approaches on both real and synthetic datasets. [ABSTRACT FROM AUTHOR]

The Quantum Approximate Optimization Algorithm (QAOA) is an algorithmic framework for finding approximate solutions to combinatorial optimization problems, derived from an approximation to the Quantum Adiabatic Algorithm (QAA). In solving combinatorial optimization problems with constraints in the context of QAOA, one needs to find a way to encode problem constraints into the scheme. In this paper, we propose and discuss several QAOA-based algorithms to solve combinatorial optimization problems with equality and/or inequality constraints. We formalize the encoding method of different types of constraints, and demonstrate the effectiveness and efficiency of the proposed scheme by providing examples and results for some well-known NP optimization problems. Compared to previous constraint-encoding methods, we argue our work leads to a more generalized framework for finding, in the context of QAOA, higher-quality approximate solutions to combinatorial problems with various types of constraints. [ABSTRACT FROM AUTHOR]

In evolutionary multiobjective optimization, effectiveness refers to how an evolutionary algorithm performs in terms of converging its solutions into the Pareto front and also diversifying them over the front. This is not an easy job, particularly for optimization problems with more than three objectives, dubbed many-objective optimization problems. In such problems, classic Pareto-based algorithms fail to provide sufficient selection pressure towards the Pareto front, whilst recently developed algorithms, such as decomposition-based ones, may struggle to maintain a set of well-distributed solutions on certain problems (e.g., those with irregular Pareto fronts). Another issue in some many-objective optimizers is rapidly increasing computational requirement with the number of objectives, such as hypervolume-based algorithms and shift-based density estimation (SDE) methods. In this paper, we aim to address this problem and develop an effective and efficient evolutionary algorithm (E3A) that can handle various many-objective problems. In E3A, inspired by SDE, a novel population maintenance method is proposed to select high-quality solutions in the environmental selection procedure. We conduct extensive experiments and show that E3A performs better than 11 state-of-the-art many-objective evolutionary algorithms in quickly finding a set of well-converged and well-diversified solutions. [ABSTRACT FROM AUTHOR]

Currently, multi-objective optimization is an important problem in various fields. This paper proposes an innovative multi-objective flower pollination algorithm combined with Monte Carlo simulation (called MFPAMC). MFPAMC incorporates the following three technologies: the flower pollination algorithm (FPA), the Monte Carlo method (MC), and a sorting function. Initially, the FPA is used to search for the optimal solution, which improves the search efficiency. When the solution obtained by the FPA is no longer updated, MC simulation is adopted to further find the optimal solution. The sorting function prevents the loss of objective function information, and the result obtained is objective and practical. A test function is used to verify whether the combination of the FPA and MC simulation can improve the search accuracy. The results confirm that the search accuracy is improved. Finally, MFPAMC is used for empirical analysis, and the experimental results are compared with those of four other methods. The comparison results confirm that the optimized results are ideal and further indicate that MFPAMC has significant potential for practical application. [ABSTRACT FROM AUTHOR]

Currently, multi-objective optimization is an important problem in various fields. This paper proposes an innovative multi-objective flower pollination algorithm combined with Monte Carlo simulation (called MFPAMC). MFPAMC incorporates the following three technologies: the flower pollination algorithm (FPA), the Monte Carlo method (MC), and a sorting function. Initially, the FPA is used to search for the optimal solution, which improves the search efficiency. When the solution obtained by the FPA is no longer updated, MC simulation is adopted to further find the optimal solution. The sorting function prevents the loss of objective function information, and the result obtained is objective and practical. A test function is used to verify whether the combination of the FPA and MC simulation can improve the search accuracy. The results confirm that the search accuracy is improved. Finally, MFPAMC is used for empirical analysis, and the experimental results are compared with those of four other methods. The comparison results confirm that the optimized results are ideal and further indicate that MFPAMC has significant potential for practical application. [ABSTRACT FROM AUTHOR]

[Display omitted] • A two-phase optimization system for solving classification problems. • Combining Auto-ML pipelines to improve overall performance. • Intelligent selection of ensemble methods. • Best results delivered by double-fault measure and 20 or 50 maximum number of base models. Automatic Machine Learning (Auto-ML) tools enable the automatic solution of real-world problems through machine learning techniques. These tools tend to be more time consuming than standard machine learning libraries, therefore, exploiting all the available resources to the full is a valuable feature. This paper presents a two-phase optimization system for solving classification problems. The system is designed to produce more robust classifiers by exploiting the different architectures that are generated while solving classification problems with Auto-ML tools, particularly AutoGOAL. In the first phase, the system follows a probabilistic strategy to find the best combination of algorithms and hyperparameters to generate a collection of base models according to certain diversity criteria; and in the second, it follows similar Auto-ML strategies to ensemble those models. The HAHA 2019 challenge corpus and the Adult dataset were used to evaluate the system. The experimental results show that: i) a better solution can be built by ensembling a subset of the already tested models; ii) the performance of ensemble methods depends on the collection of base models used; and, iii) ensuring diversity using the double-fault measure produces better results than the disagreement measure. The source code is available online for the research community. [ABSTRACT FROM AUTHOR]

This paper investigates the distributed control of multi-agent systems (MASs) with objective optimization on directed detail-balanced networks, in which the global optimization function is expressed as a convex combination of local objectives of agents. First, a directed and detail-balanced network depending on the weights of an optimization function is constructed, and a distributed consensus protocol with gradients of local objectives is proposed over the designed network. Using Lyapunov stability theory and a projection technique, we prove that the proposed protocol not only makes all agents achieve consensus in a fixed-time interval but can also solve the global optimization problem asymptotically. Moreover, the optimization problem with box constraints is studied, and a δ -exact penalty method is employed to eliminate the constraints. Similarly, a distributed fixed-time consensus protocol with gradient measurement is developed, and we prove that the optimal solution can be reached asymptotically. Finally, two examples are presented to show the efficacy of the theoretical results. [ABSTRACT FROM AUTHOR]

• Proposes a distributed adaptive optimization spiking neural P system with a distributed population structure and a new adaptive learning rate considering population diversity. • Extensive experiments on knapsack problems show that DAOSNPS gains much better and more stable solutions than OSNPS, AOSNPS and other two optimization algorithms. An optimization spiking neural P system (OSNPS) aims to obtain the approximate solutions of combinatorial optimization problems without the aid of evolutionary operators of evolutionary algorithms or swarm intelligence algorithms. To develop the promising and significant research direction, this paper proposes a distributed adaptive optimization spiking neural P system (DAOSNPS) with a distributed population structure and a new adaptive learning rate considering population diversity. Extensive experiments on knapsack problems show that DAOSNPS gains much better solutions than OSNPS, adaptive optimization spiking neural P system, genetic quantum algorithm and novel quantum evolutionary algorithm. Population diversity and convergence analysis indicate that DAOSNPS achieves a better balance between exploration and exploitation than OSNPS and AOSNPS. [ABSTRACT FROM AUTHOR]

This paper proposes a particle swarm optimization with strategy dynamics (SDPSO) to solve single-objective optimization problems. SDPSO consists of four PSO search strategies. Evolutionary game theory is introduced to control the population state. In evolutionary game theory, through the interaction between players, better strategies will eventually dominate among the players. By extending this idea to PSO, a selection mechanism and a mutation mechanism are proposed. By using the selection mechanism, the adoption probability of the high payoff strategies will increase. The mutation mechanism can examine the stability of the incumbent strategy to evolutionary pressures. The performance of SDPSO is compared with 14 algorithms on the CEC 2014 test suite. The results show that SDPSO has the highest rank. SDPSO is applied to solve a real-world problem. SDPSO can find the best mean results comparing with 4 algorithms. The findings show that the proposed evolutionary game theory-based framework can adaptively control the population state. This study proposes a new application of evolutionary game theory to the design of swarm intelligence and contributes to a better understanding of the usefulness of the evolutionary game theory in the optimization method. The source codes of SDPSO are available at https://github.com/zi-ang-liu/SDPSO. [ABSTRACT FROM AUTHOR]

Particle swarm optimization (PSO) is a population-based random-search optimization technique that has become an increasingly important branch of swarm intelligence studies. Population diversity is an effective measurement that shows the distribution of particles in a search space. In this paper, we propose a diversity-based PSO algorithm that is combined with multiple techniques, and population diversity is used as a search-strategy selection criterion for particles. When diversity is high, we suggest implementing a search strategy that has a strong exploration ability. When diversity is low, we suggest implementing a search strategy that has a strong exploitation ability. In addition to our diversity-based method, we introduce a gradient-based local-search technique, multi-crossover operation, and disturbance strategy to help improve the performance of the proposed algorithm. In the experiments, we compare the proposed algorithm with 10 advanced PSO variants based on 40 widely used benchmark functions, including the CEC2017 benchmark. The results indicate that the proposed algorithm yields a better solution accuracy and convergence speed than those of other PSO variants. [ABSTRACT FROM AUTHOR]

• A new algorithm, called BTLBO, is proposed to balance between exploration and exploitation. • A new Tutoring Phase is added to TLBO to accelerate the convergence to the global optimum. • A new Restarting Phase is added to TLBO to promote convergence to the global optimum. • Control parameters are self-adaptively tuned. • The superiority of BTLBO is verified by solving standard benchmarks and real world optimization problems. Teaching-learning-based optimization (TLBO) is a population-based metaheuristic algorithm which simulates the teaching and learning mechanisms in a classroom. The TLBO algorithm has emerged as one of the most efficient and attractive optimization techniques. Even though the TLBO algorithm has an acceptable exploration capability and fast convergence speed, there may be a possibility to converge into a local optimum during solving complex optimization problems and there is a need to keep a balance between exploration and exploitation capabilities. Hence, a Balanced Teaching-Learning-Based Optimization (BTLBO) algorithm is proposed in this paper. The proposed BTLBO algorithm is a modification of the TLBO algorithm and it consists of four phases: (1) Teacher Phase in which a weighted mean is used instead of a mean value for keeping the diversity, (2) Learner Phase, which is same as the learner phase of basic TLBO algorithm, (3) Tutoring Phase, which is a powerful local search for exploiting the regions around the best ever found solution, and (4) Restarting Phase, which improves exploration capability by replacing inactive learners with new randomly initialized learners. An acceptable balance between the exploration and exploitation capabilities is achieved by the proposed BTLBO algorithm. To evaluate the performance of BTLBO algorithm, several experimental studies are conducted on standard benchmark suits and the results are compared with several TLBO variants and state-of-the-art population-based optimization algorithms. The results are in excellent agreement and confirm the efficiency of BTLBO algorithm with accelerated exploitation and exploration capabilities with an appropriate balance between such criteria for solving complex optimization problems. [ABSTRACT FROM AUTHOR]

• A hybridization approach for improving population-based meta-heuristics is proposed. • The worst candidates in a population are replaced by predicted candidates. • The calibration problem based on the population is solved to create candidates. • The hybridized ten meta-heuristics outperform their counterparts. The recent proliferation of population-based meta-heuristics designed for solving optimization problems and their successes confirm that more promising techniques inspired by physical phenomena or biological systems are desired. Therefore, in this paper, a novel hybridization approach is proposed to improve the performance of optimization algorithms. The approach replaces a small number of worst solutions obtained by a meta-heuristic with predicted candidates without altering its search operators. Specifically, a target fitness value of the predicted candidate is determined based on the fitness of the population and a search strategy. Then, a calibration problem is solved to infer its decision variables. In this study, the proposed hybridization technique is applied to ten state-of-the-art population-based algorithms. The meta-heuristics and hybrids are evaluated on 82 functions, four engineering problems, and a new challenging problem of estimating a constant in Markov's inequality using minimal polynomials of different degrees. The experimental results reveal the superiority of the hybrids over their counterparts and confirm the suitability of the proposed approach for improving the efficiency of meta-heuristics. [ABSTRACT FROM AUTHOR]

• Elite particles in the population are selected based on the target value of the particles and the mutual distance between the particles. • Based on a unique small group of particles and the nearest three particles, all particles are close to the local elite particles to find multiple global peaks. The final solution has very high accuracy. Niching is an important technique for multimodal optimization. Most existing niching methods require specification of certain niching parameters in order to perform well. But these parameters are usually difficult to set because they depend on the problem. The particle swarm optimization algorithm using the ring neighborhood topology does not require any niche parameters, but the determination of the particle neighborhood in this method is based on the subscript of the particle, and the result fails to achieve the best performance. For better performance, in this paper, a particle swarm optimization algorithm based on the ring neighborhood topology of Euclidean distance between particles is proposed, which is called the close neighbor mobility optimization algorithm. The algorithm mainly includes the following three strategies: elite selection mechanism, close neighbor mobility strategy and modified DE strategy. It mainly uses the Euclidean distance between particles. Each particle forms its own unique niche, evolves in a local scope, and finally locates multiple global optimal solutions with high precision. The algorithm greatly improves the accuracy of the particle. The experimental results show that the close neighbor mobility optimization algorithm has better performance than most single-objective multi-modal algorithms. [ABSTRACT FROM AUTHOR]

Abstract Many optimization algorithms have adopted scale-free networks to improve the search ability. However, most methods have merely changed their population topologies into those of scale-free networks; their experimental results cannot verify that these algorithms have superior performance. In this paper, we propose a scale-free artificial bee colony algorithm (SFABC) in which the search is guided by a scale-free network. The mechanism enables the SFABC search to follow two rules. First, the bad food sources can learn more information from the good sources of their neighbors. Second, the information exchange among good food sources is relatively rare. To verify the effectiveness of SFABC, the algorithm is compared with the original artificial bee colony algorithm (ABC), several advanced ABC variants, and other metaheuristic algorithms on a wide range of benchmark functions. Experimental results and statistical analyses indicate that SFABC obtains a better balance between exploration and exploitation during the optimization process and that, in most cases, it can provide a competitive performance of the benchmark functions. We also verify that scale-free networks can not only improve the optimization performance of ABC but also enhance the search ability of other metaheuristic algorithms, such as differential evolution (DE) and the flower pollination algorithm (FPA). [ABSTRACT FROM AUTHOR]

Firefly algorithm (FA) is an effective optimization technique based on swarm intelligence, which has been successfully applied to various practical engineering problems. In this paper, a new dynamic FA (called NDFA) is proposed for demand estimation of water resources in Nanchang city of China. First, a dynamic parameter strategy is used to avoid manually adjusting the step factor. Second, three estimation models in different forms (linear, exponential and hybrid) are developed in terms of the historical water use and local economic structure. Third, normalization method is utilized to eliminate the influences of different units of data. In the experiments, water use in Nanchang city from 2003 to 2015 is considered as a case study. The data from 2003 to 2012 are used for finding the optimal weights of the models, and the rest of data (2013–2015) are applied to test the models. Computational results show that all five FA variants can achieve promising solutions. The proposed NDFA obtains better performance than four other FA variants, and its prediction accuracy is up to 97.91%. Finally, the water demand in Nanchang city from 2017 to 2020 is predicted. [ABSTRACT FROM AUTHOR]

Plenty of metaheuristics are proposed each year. Such methods are frequently based on various concepts, are described with use of different nomenclature and are compared on different benchmarks. As a result it is often hard for practitioners to evaluate how useful particular methods may be. In a number of critical papers the need for careful theoretical and empirical analysis of new metaheuristics has been addressed. The present study presents such an analysis of an Across Neighborhood Search algorithm (ANS), a simple and relatively new method proposed in 2016. Firstly, it is shown that ANS is mainly a simplified combination of a few Particle Swarm Optimization variants, not a fully novel approach. Secondly, it is shown that ANS is structurally biased, hence artificially samples some parts of the search space more frequently than the other parts. Thirdly, the difficulties with the choice of one of the ANS control parameters are discussed. Finally, ANS is empirically tested against 22 other metaheuristics proposed during the 1960–2015 period on CEC2011, CEC2014 and CEC2017 problems, showing moderate performance. Overall, ANS turned out to be better than older competitors, but poorer than the majority of methods proposed during the last 10 years. [ABSTRACT FROM AUTHOR]

The Intelligent Water Drop (IWD) algorithm is a recent stochastic swarm-based method that is useful for solving combinatorial and function optimization problems. In this paper, we propose an IWD ensemble known as the Master-River, Multiple-Creek IWD (MRMC-IWD) model, which serves as an extension of the modified IWD algorithm. The MRMC-IWD model aims to improve the exploration capability of the modified IWD algorithm. It comprises a master river which cooperates with multiple independent creeks to undertake optimization problems based on the divide-and-conquer strategy. A technique to decompose the original problem into a number of sub-problems is first devised. Each sub-problem is then assigned to a creek, while the overall solution is handled by the master river. To empower the exploitation capability, a hybrid MRMC-IWD model is introduced. It integrates the iterative improvement local search method with the MRMC-IWD model to allow a local search to be conducted, therefore enhancing the quality of solutions provided by the master river. To evaluate the effectiveness of the proposed models, a series of experiments pertaining to two combinatorial problems, i.e., the travelling salesman problem (TSP) and rough set feature subset selection (RSFS), are conducted. The results indicate that the MRMC-IWD model can satisfactorily solve optimization problems using the divide-and-conquer strategy. By incorporating a local search method, the resulting hybrid MRMC-IWD model not only is able to balance exploration and exploitation, but also to enable convergence towards the optimal solutions, by employing a local search method. In all seven selected TSPLIB problems, the hybrid MRMC-IWD model achieves good results, with an average deviation of 0.021% from the best known optimal tour lengths. Compared with other state-of-the-art methods, the hybrid MRMC-IWD model produces the best results (i.e. the shortest and uniform reducts of 20 runs) for all13 selected RSFS problems. [ABSTRACT FROM AUTHOR]