Defining urban graphic heritage for economic development in the UK and China

What new perspectives can graphic design contribute to design for heritage? This paper provides answers to this question by confirming the meaning of heritage and the value of research in the context of a vibrant and evolving creative industries in the United Kingdom and China. Heritage has become an important topic for research in recent decades, and now features as a priority area with research councils. Increasingly, it is framed as cultural heritage, but the meaning of culture is unclear. In outlining the challenges associated with rapid urban development in China, and the importance of design, planning and heritage, a framework for analysing urban graphic heritage is proffered alongside empirical research from the United Kingdom and China. Despite its importance being overlooked in heritage discourse as well as contemporary reviews of the creative industries, graphic design is shown to provide a unique overarching perspective for the design challenges associated with the human experience of urban heritage

Multiswarm comprehensive learning particle swarm optimization for solving multiobjective optimization problems

<div><p>Comprehensive learning particle swarm optimization (CLPSO) is a powerful state-of-the-art single-objective metaheuristic. Extending from CLPSO, this paper proposes multiswarm CLPSO (MSCLPSO) for multiobjective optimization. MSCLPSO involves multiple swarms, with each swarm associated with a separate original objective. Each particle’s personal best position is determined just according to the corresponding single objective. Elitists are stored externally. MSCLPSO differs from existing multiobjective particle swarm optimizers in three aspects. First, each swarm focuses on optimizing the associated objective using CLPSO, without learning from the elitists or any other swarm. Second, mutation is applied to the elitists and the mutation strategy appropriately exploits the personal best positions and elitists. Third, a modified differential evolution (DE) strategy is applied to some extreme and least crowded elitists. The DE strategy updates an elitist based on the differences of the elitists. The personal best positions carry useful information about the Pareto set, and the mutation and DE strategies help MSCLPSO discover the true Pareto front. Experiments conducted on various benchmark problems demonstrate that MSCLPSO can find nondominated solutions distributed reasonably over the true Pareto front in a single run.</p></div

Ranks of MSCLPSO, CMPSO, MOEA/D, and NSGA-II in term of the mean IGD results on all the benchmark problems.

<p>Ranks of MSCLPSO, CMPSO, MOEA/D, and NSGA-II in term of the mean IGD results on all the benchmark problems.</p

Final single-objective best solutions obtained by the swarms of MSCLPSO on all the benchmark problems.

<p>Final single-objective best solutions obtained by the swarms of MSCLPSO on all the benchmark problems.</p

Characteristics of all the benchmark problems.

<p>Characteristics of all the benchmark problems.</p

IGD results of the MSCLPSO variants, CMPSO, MOEA/D, and NSGA-II on all the benchmark problems.

<p>IGD results of the MSCLPSO variants, CMPSO, MOEA/D, and NSGA-II on all the benchmark problems.</p

Basic architecture of MSCLPSO.

<p>Basic architecture of MSCLPSO.</p

Final nondominated solutions obtained on the UF benchmark problems.

<p>(a) MSCLPSO in the best run and CMPSO in the best run on UF1 (b) MSCLPSO in the best run and CMPSO in the best run on UF2 (c) MSCLPSO in the best run and NSGA-II in the best run on UF7 (d) MSCLPSO in the best run on UF8 (e) MSCLPSO in the best run on UF9.</p

Algorithm parameters of the MSCLPSO variants.

<p>Algorithm parameters of the MSCLPSO variants.</p

IGD results of MSCLPSO using some different parameter settings.

<p>IGD results of MSCLPSO using some different parameter settings.</p