Cooperative Co-evolution (CC) is an evolutionary computation framework that can be used to solve high dimensional optimization problems via a ‘divide-and-conquer’ mechanism. However, the main challenge when using this framework lies in problem decomposition. That is, deciding how to allocate decision variables to a particular sub-problem, especially interacting decision variables. Existing decomposition methods are typically computationally expensive. In this paper, we propose a new decomposition method, which we call Recursive Differential Grouping (RDG), by considering the interaction between decision variables based on non-linearity detection. RDG recursively examines the interaction between a selected decision variable and the remaining variables, placing all interacting decision variables into the same sub-problem. We use analytical methods to show that RDG can be used to efficiently decompose a problem, without explicitly examining all pairwise variable interactions. We evaluated the efficacy of the RDG method using large scale benchmark optimization problems. Numerical simulation experiments showed that RDG greatly improved the efficiency of problem decomposition in terms of time complexity. Significantly, when RDG was embedded in a CC framework, the optimization results were better than results from seven other decomposition methods.

Trust and trustworthiness are of great importance in social and human systems, especially when considering managerial and economic decision-making. In this paper, we investigate the emergent dynamics of an evolutionary game-theoretic model-the N-player evolutionary trust game-consisting of three types of players: 1) an investor; 2) a trustee who is trustworthy; and 3) a trustee who is untrustworthy. Here, we limit the interactions between players to local neighborhoods defined by a specific spatial topology or social network. Players are able to adjust their game-playing strategies using an evolutionary update rule based on the payoffs obtained by their neighbors. Through comprehensive simulation experiments, we find that trust can be promoted when players interact on a social network despite a substantial number of untrustworthy individuals in the initial population. These results differ from findings reported for an unstructured population of the same game, where the existence of a single untrustworthy individual would eliminate trust completely. We compare the dynamics of the model with different social network densities and structures (e.g., from regular lattices to scale-free and random networks). We observe that the levels of trust vary under different network structures, and the level is correlated with how “difficult” the game is. When game conditions are easy (i.e., low temptation to defect and/or almost no initial untrustworthy trustees), homogeneous networks with higher densities can promote higher levels of trust. However, when the game becomes harder, heterogeneous social networks with lower densities are able to promote higher levels of trust and global net wealth.

The planetary boundary layer height (PBLH) is one of the key factors in influencing the dispersion of the air pollutants in the troposphere and, hence, the air pollutant concentration on ground level. For this reason, accurate air pollutant concentration depends on the performance of PBLH prediction. Recently, ceilometers, a lidar instrument to measure cloud base height, have been used by atmospheric scientists and air pollution control authorities to determine the mixing level height (MLH) in improving forecasting and understanding the evolution of aerosol layers above ground at a site. In this study, ceilometer data at an urban (Lidcombe) and a rural (Merriwa) location in New South Wales, Australia, were used to investigate the relationship of air pollutant surface concentrations and surface meteorological variables with MLH, to validate the PBLH prediction from two air quality models (CCAM-CTM and WRF-CMAQ), as well as to understand the aerosol transport from sources to the receptor point at Merriwa for the three case studies where high PM₁₀ concentration was detected in each of the three days. The results showed that surface ozone and temperature had a positive correlation with MLH, while relative humidity had negative correlation. For other pollutants (PM₁₀, PM_2.5, NO₂), no clear results were obtained, and the correlation depended on the site and regional emission characteristics. The results also showed that the PBLH prediction by the two air quality models corresponded reasonably well with the observed ceilometer data and the cause and source of high PM₁₀ concentration at Merriwa can be found by using ceilometer MLH data to corroborate back trajectory analysis of the transport of aerosols to the receptor point at Merriwa. Of the three case studies, one had aerosol sources from the north and north west of Merriwa in remote NSW, where windblown dust is the main source, and the other two had sources from the south and south east of Merriwa, where anthropogenic sources dominate.

Constrained multi-objective optimization problems (CMOPs) are generally more challenging than unconstrained problems. This in part can be attributed to the infeasible region generated by the constraint functions, the interaction between constraints and objectives, or both. In this paper, we explore the relationship between the performance of constrained multi-objective evolutionary algorithms (CMOEAs) and the instance characteristics of CMOP using Instance Space Analysis (ISA). To do this, we extend recent work on Landscape Analysis features for characterising CMOPs. Specifically, we introduce new features to describe the multi-objective-violation landscape, formed by the interaction between constraint violation and multi-objective fitness. Detailed evaluation of the algorithm footprints, spanning eight CMOP benchmark suites and fifteen CMOEAs, demonstrates that ISA effectively captures the strength and weakness of the CMOEAs. We conclude that two characteristics, the isolation of non-dominate set and the correlation between constraints and objectives evolvability, have the greatest impact on algorithm performance. However, the current benchmarks problems lack of diversity to represent the real-world problems and to fully reveal the efficacy of CMOEAs evaluated.