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<title>Swarm experiments</title>
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<a href="..">..Back to EliasHasle.github.io.</a>
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<h1>Swarm experiments</h1>
<p>Here is a small collection of experiments that I made in a Swarm Intelligence course at NTNU in Ålesund.</p>
<dl class="experiments" id="swarmexperiments">
<dt><a href="Behavior_Arrival.html">Arrival behavior</a></dt>
<dd><br /></dd>
<dt><a href="Behavior_Seek_comparison.html">Two Seek implementations</a></dt>
<dd>The red one is smarter. Refresh the page multiple times to get new random scenarios.</dd>
<dt><a href="Pack_hunting_Non-PID.html">Pack hunting 1</a></dt>
<dd>With single target per hunter and improved Pursue implementation. Suffers a bit from conflicts due to the (necessary) separation behavior.</dd>
<dt><a href="Pack_hunting_experimental_PID_averaged_target_online-optimization.html">Pack hunting 2</a></dt>
<dd>Experimental, using combined behaviors, weighted target points, PID controllers and online optimization. It suffers a bit from PID being more suited for docking than pursuing.</dd>
<dt><a href="PSO_Visual_with_sliders.html">Simple PSO searching in a 2D plane</a></dt>
<dd>With interactive modification of PSO parameters</dd>
<dt><a href="PSO_Visual_with_parameters_oscillating_in_dissonance.html">PSO dissonance</a></dt>
<dd>Experiment with PSO parameters sweeping over their range at frequencies related by the golden ratio, and respawning particles upon convergence to global best known.</dd>
<dt><a href="PSO_Path.html">PSO_Path</a></dt>
<dd>A simplistic short-sighted path planning using PSO, with interactive controls</dd>
<dt><a href="PSO_Path_optimized.html">PSO_Path_optimized</a></dt>
<dd>A variant of the above that uses the last PSO state in previous position as starting point in the new position and runs only a single iteration per decision, compensating with a more particles and larger space (still only selects a direction without building a search tree)</dd>
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