## 5 Memetic Algorithm Based Multi-Objective Coverage Optimization  
In this paper, MOCADMA uses the dynamic local search strategy and a local algorithm scheduling mechanism to adapt to the selection of local search algorithms; and increases the optimal storage strategy to accelerate the convergence speed of the algorithm. The pseudo code of MOCADMA is described as follows:  
\[\begin{array}{l}\textit{Pseudo code of MOCADMA}\\ \textit{Given information}:\textit{the target field}\textit{D},\textit{the points of interest}\{c_{1},c_{2},...,c_{4}\}\,\\ \textit{the sink node}(\textit{Sink}),\textit{the set of sensor nodes}\textit{T}=\{t_{1},t_{2},...,t_{n}\},\\ \textit{the candidate population}(\textit{CP}),\textit{the optimal population}(\textit{P})\\ \textit{the candidate population size}(\textit{pts}),\textit{the optimal population size}(\textit{pos}),\\ \textit{the crossover probability}(\textit{pcross}),\textit{the mutation probability}(\textit{pmutation})\\ \textit{maximum iterations}(\textit{mi}),\textit{parameters used in the fitness function}\\ \textit{step}(\textsc{A}):\textit{Deploying}\textit{T}\textit{ over D}\\ \textit{for}\textit{i}\leftarrow\textit{1}\textit{to n do}\\ \textit{Sink}\leftarrow(\textit{location}_{i},\textit{r}_{i},\textit{re}_{i}, \textit{residual\_energy}_{i})\\ \textit{endfor}\\ \textit{step}(\textit{B}):\textit{D}\rightarrow\textit{M}\times\textit{N virtual cells}\\ \textit{step}(\textit{C}):\textit{for}\textit{i}\leftarrow\textit{1}\textit{to n do}\\ \textit{for}\textit{j}\leftarrow\textit{1}\textit{to k do}\\ \textit{Calculate}\textit{p}(\textit{t}_{i},\textit{c}_{j})\\ \textit{endfor}\\ \textit{endfor}\\ \textit{for}\textit{i}\leftarrow\textit{1}\textit{to n do}\\ \textit{calculate}\mid\textit{s}_{i}\mid\\ \textit{endfor}\\ \textit{repeat}\\ \textit{for}\textit{i}\leftarrow\textit{1}\textit{to}\textit{M}\textit{do}\\ \textit{for}\textit{j}\leftarrow\textit{1}\textit{to}\textit{N do}\\ \textit{X}_{{}_{i,j}}\leftarrow\textit{t}=\textit{RouletteWheelSelect}(T,i,j)\\ \textit{endfor}\\ \textit{endfor}\\ \textit{endfor}\\ \textit{\Re}\leftarrow\textit{EncodeChromosome}(\textit{Matrix}_{i=1,j=1}^{- \textit{u},\textit{j}=N}(\textit{X}_{{}_{i,j}}))\\ \textit{if}(\textit{\Re}\textit{CP})\textit{then}\\ \textit{CP}\leftarrow\aleph\\ \textit{endif}\\ \textit{until the number of individuals in CP reaches pts  
**20508**  
_for \(i\gets 1\) to pts do_  
_Calculate \(f(\mathbf{R}_{i})\)_  
_endfor_  
_repeat_  
_Select the Pareto - optimal individual in CP and add it into P_  
_until the number of individuals in P reaches pos_  
_repeat_  
\(step(D):\) _for \(i\gets 1\) to Numo/Crossover do_  
\(Chromod\gets Select(P)\)  
\(Chromos\gets Select(P)\)  
\(OP\gets OP+Crossover(A,B,cross)\)  
_endfor_  
\(step(E):\) _for \(i\gets 1\) to Numo/Mutation do_  
\(Chromod\gets Select(OP)\)  
\(OP\gets OP+Mutate(A,pmutation)\)  
_endfor_  
\(step(F):\) _/randomly and alternately select some local search algorithms to make local search_  
\(P_{i}\gets Select\)_TabSearch(\(P\bigcup OP\))_  
\(P_{i}\gets Hill\)_ClimbingSearch(\(P\bigcup OP\))_  
\(P_{i}\gets Adaptive\)_DirectionalSearch(\(P\bigcup OP\))_  
\(CP\)\(\leftarrow Evaluate(P_{1},P_{2},P_{3})\)_  
\(step(G):\) _repeat_  
_Select the Pareto - optimal individual in CP and add it into P_  
_until the number of individuals in P reaches pos_  
_until the number of iterations reaches mi_  
\(step(H):\) _Get current optimal deployment solution from P_  
\(step(I):\) _Execute current optimal deployment solution over T_  
MOCADMA includes iterated evolutionary operations of memetic algorithm and dynamic local search strategy, which is described in detail as follows:  
(A). The Sink Collects the Information of Sensor Nodes  
After initial uniform deployment, every sensor node is in the \"detecting\" state and sends its location information (_#location_), maximum ideal sensing radius (_#r_), sensing error value (_#re_) and energy information (_#residual_Energy_) to the sink by flooding communication. (B).