https://iasei.org/journal/index.php/itsi/issue/feed 2020-12-22T02:40:30-07:00 Open Journal Systems <p><strong>IASEI Transactions on Swarm Intelligence is the peer-reviewed and fast-track (within one month) online publication dedicated to reporting on research and developments in the multidisciplinary fields of swarm intelligence.</strong></p> <p>–The fastest online publication of academic research work and survey</p> <p>–Latest original and distinguished research work and advancement, overseeing survey and comments</p> <p>–Paper types: Regular, Communications, Survey, …</p> <p>–Two types of publication are freely chosen: Classic Manner (Free) or Open Access (Charge apply)</p> <p>–Published papers will be sent to Indexing Databases timely.</p> <p>-Topics</p> <ul> <li class="show">Modeling, emulation and analysis of collective or populated systems such as biological swarms, natural phenomenon groupings, social insect colonies/schools, human crowds, as well as any other swarm intelligence systems and multi-agent systems, etc.;</li> <li class="show">Theoretical analysis and empirical research in a variety of swarm intelligence algorithms and/or population-based algorithms, like particle swarm optimization, ant colony optimization, fireworks algorithm, brainstorming optimization, artificial bee colony, foraging algorithm, culture algorithm, artificial immune system, any other nature-inspired optimization algorithms, etc.</li> <li class="show">Design, analysis and implementation of swarm intelligence algorithms, swarm robotics and/or multi-agent systems</li> <li class="show">Applications of any swarm intelligence algorithms and models to real-world problems.</li> </ul> <ul> <li class="show"><em>Swarm-based optimization techniques</em></li> <li class="show"><em>Natural computing</em></li> <li class="show"><em>Fireworks algorithms</em></li> <li class="show"><em>Multi-agent theories</em></li> <li class="show"><em>Cooperative theories</em></li> <li class="show"><em>Optimization theories</em></li> <li class="show"><em>Data mining</em></li> <li class="show"><em>Machine learning</em></li> <li class="show"><em>Pattern recognition</em></li> <li class="show"><em>Automatic control</em></li> </ul> <p>–Advantages</p> <ul> <li class="show">The time for a submission to be accepted/published online or rejected must not be more than <strong>1 month</strong>.</li> <li class="show">No revision is allowed for keeping the fast publication in 1-month, therefore, any submission must be ready to publish when its submitting. Any submission must be prepared in accordance with the IASEI paper-format by using the provided IASEI paper- template online.</li> <li class="show">Each submission will be assigned to one Associate Editor who takes charge of the peer-review of the submission and has the responsibility of evaluating the quality of the submission, and then make a recommendation (accept/reject) within 1-month.</li> </ul> <p>IASEI Transactions on Swarm Intelligence is the online journal published by International Association of Swarm and Evolutionary Intelligences which try to guarantee fast publication for each accepted manuscript within one month then one volume will be collected once a year.</p> https://iasei.org/journal/index.php/itsi/article/view/3 2020-12-17T02:13:36-07:00 Junzhi Li ljz@pku.edu.cn Ying Tan ytan@pku.edu.cn

<p>Heuristic algorithms are able to optimize objective functions efficiently<br>because they use intelligently the information about the objective functions. Thus, information utilization is critical to the performance of heuristics. However, the concept of information utilization has remained vague and abstract because there is no reliable metric to reflect the extent to which the information about the objective function is utilized by heuristic algorithms. In this paper, the metric of information utilization ratio (IUR) is defined, which is the ratio of the utilized information quantity over the acquired information quantity in the search process. The IUR proves to be well-defined. Several examples of typical heuristic algorithms are given to demonstrate the procedure of calculating the IUR. Empirical evidences on the correlation between the IUR and the performance of a heuristic are also provided. The IUR can be an index of how finely an algorithm is designed and guide the invention of new heuristics and the improvement of existing ones.</p>

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