Evolutionary Economics の最近の発展

Over the ages, nature has constantly been a rich source of inspiration for science, with much still to discover about and learn from. Swarm Intelligence (SI), a major branch of artificial intelligence, was rendered to model the collective behavior of social swarms in nature. Ultimately, Particle Swarm Optimization algorithm (PSO) is arguably one of the most popular SI paradigms. Over the past two decades, PSO has been applied successfully, with good return as well, in a wide variety of fields of science and technology with a wider range of complex optimization problems, thereby occupying a prominent position in the optimization field. However, through in-depth studies, a number of problems with the algorithm have been detected and identified; e.g., issues regarding convergence, diversity, and stability. Consequently, since its birth in the mid-1990s, PSO has witnessed a myriad of enhancements, extensions, and variants in various aspects of the algorithm, specifically after the twentieth century, and the related research has therefore now reached an impressive state. In this paper, a rigorous yet systematic review is presented to organize and summarize the information on the PSO algorithm and the developments and trends of its most basic as well as of some of the very notable implementations that have been introduced recently, bearing in mind the coverage of paradigm, theory, hybridization, parallelization, complex optimization, and the diverse applications of the algorithm, making it more accessible. Ease for researchers to determine which PSO variant is currently best suited or to be invented for a given optimization problem or application. This up-to-date review also highlights the current pressing issues and intriguing open challenges haunting PSO, prompting scholars and researchers to conduct further research both on the theory and application of the algorithm in the forthcoming years.

Major Advances in Particle Swarm Optimization: Theory, Analysis, and Application

Using a novel multi-variable grey model to forecast the electricity consumption of Shandong Province in China

A large number of intelligent algorithms based on social intelligent behavior have been extensively researched in the past few decades, through the study of natural creatures, and applied to various optimization fields. The learning-based intelligent optimization algorithm (LIOA) refers to an intelligent optimization algorithm with a certain learning ability. This is how the traditional intelligent optimization algorithm combines learning operators or specific learning mechanisms to give itself some learning ability, thereby achieving better optimization behavior. We conduct a comprehensive survey of LIOAs in this paper. The research includes the following sections: Statistical analysis about LIOAs, classification of LIOA learning method, application of LIOAs in complex optimization scenarios, and LIOAs in engineering applications. The future insights and development direction of LIOAs are also discussed.

A Survey of Learning-Based Intelligent Optimization Algorithms

Particle swarm optimization (PSO) is one of the most well-regarded swarm-based algorithms in the literature. Although the original PSO has shown good optimization performance, it still severely suffers from premature convergence. As a result, many researchers have been modifying it resulting in a large number of PSO variants with either slightly or significantly better performance. Mainly, the standard PSO has been modified by four main strategies: modification of the PSO controlling parameters, hybridizing PSO with other well-known meta-heuristic algorithms such as genetic algorithm (GA) and differential evolution (DE), cooperation and multi-swarm techniques. This paper attempts to provide a comprehensive review of PSO, including the basic concepts of PSO, binary PSO, neighborhood topologies in PSO, recent and historical PSO variants, remarkable engineering applications of PSO, and its drawbacks. Moreover, this paper reviews recent studies that utilize PSO to solve feature selection problems. Finally, eight potential research directions that can help researchers further enhance the performance of PSO are provided. 

Particle Swarm Optimization: A Comprehensive Survey

A novel multi-swarm particle swarm optimization with dynamic learning strategy

Memory-based prisoners dilemma game with conditional selection on networks

Evolutionary snowdrift game with rational selection based on radical evaluation

With the consideration of the heterogeneity of investors, an investors’ power-based game is proposed (Xu et al., 2018), where payoffs of defectors depend on the efficiency of market and the related-power against cooperators. Directed by the special structure of the power-based game, effects of investors’ power correlations in the power-based game on networks are studied in this paper. The power correlations, also called the degree correlation in the traditional theory of graph, is usually measured by the assortativity coefficient r . Firstly, we theoretically show that the expected payoff of a cooperator is more than that of a defector as the level of assotativity is high enough. Meanwhile, our simulation results show that, an increment of assortativity coefficient raises the average payoffs of cooperators and boosts cooperations, verifying the theoretical inference. Furthermore, as the market efficiency α swings, the density of cooperators will be higher and more stable on the network with the larger r . As the network of investment in real world may possess both of the properties of BA scale-free networks and assortative networks, the findings may be helpful in managing the emergence of cooperative behaviors.

Effects of investors' power correlations in the power-based game on networks

In this paper, we introduce the particle swarm optimization into the social dilemma, and investigate the effect of particle swarm optimization on the evolutionary traveler’s dilemma game with the continuous version for different networks. In this modified model, each individual updates its strategy according to two terms, the most profitable strategy throughout its history and the imitation of the currently best strategy gained from its community. The simulation result reveals that the proposed learning method greatly facilitates the emergence and maintenance of cooperation in comparison with the traditional Fermi dynamics. Additionally, the particle swarm method makes an effective influence on the spatial interaction. When the reward/punishment is small, social interaction helps high-value claiming prevail in the system, leading to the diversity of population under a large strategy interval. And low-value claiming also benefits through frequent communication under a small strategy interval. Moreover, the cooperation level is enhanced by the increasing self-cognition at the expense of disorder and breakdown of community since each competing individual is able to gain more payoffs by adjusting its own superior strategy independently.

Wenxing Ye

Papers

A novel multi-swarm particle swarm optimization with dynamic learning strategy

Memory-based prisoners dilemma game with conditional selection on networks

Evolutionary snowdrift game with rational selection based on radical evaluation

Effects of investors' power correlations in the power-based game on networks

Evolutionary traveler's dilemma game based on particle swarm optimization